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Principal component analysis (PCA) is alineardimensionality reduction technique with applications inexploratory data analysis, visualization anddata preprocessing.
The data arelinearly transformed onto a newcoordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.
Theprincipal components of a collection of points in areal coordinate space are a sequence ofunit vectors, where the-th vector is the direction of a line that best fits the data while beingorthogonal to the first vectors. Here, a best-fitting line is defined as one that minimizes the average squaredperpendiculardistance from the points to the line. These directions (i.e., principal components) constitute anorthonormal basis in which different individual dimensions of the data arelinearly uncorrelated. Many studies use the first two principal components in order to plot the data in two dimensions and to visually identify clusters of closely related data points.[1]
Principal component analysis has applications in many fields such aspopulation genetics,microbiome studies, andatmospheric science.[2]
When performing PCA, the first principal component of a set of variables is the derived variable formed as a linear combination of the original variables that explains the most variance. The second principal component explains the most variance in what is left once the effect of the first component is removed, and we may proceed through iterations until all the variance is explained. PCA is most commonly used when many of the variables are highly correlated with each other and it is desirable to reduce their number to anindependent set.The first principal component can equivalently be defined as a direction that maximizes the variance of the projected data. The-th principal component can be taken as a direction orthogonal to the first principal components that maximizes the variance of the projected data.
For either objective, it can be shown that the principal components areeigenvectors of the data'scovariance matrix. Thus, the principal components are often computed byeigendecomposition of the data covariance matrix orsingular value decomposition of the data matrix. PCA is the simplest of the true eigenvector-based multivariate analyses and is closely related tofactor analysis. Factor analysis typically incorporates more domain-specific assumptions about the underlying structure and solves eigenvectors of a slightly different matrix. PCA is also related tocanonical correlation analysis (CCA). CCA defines coordinate systems that optimally describe thecross-covariance between two datasets while PCA defines a neworthogonal coordinate system that optimally describes variance in a single dataset.[3][4][5][6]Robust andL1-norm-based variants of standard PCA have also been proposed.[7][8][9][6]
PCA was invented in 1901 byKarl Pearson,[10] as an analogue of theprincipal axis theorem in mechanics; it was later independently developed and named byHarold Hotelling in the 1930s.[11] Depending on the field of application, it is also named the discreteKarhunen–Loève transform (KLT) insignal processing, theHotelling transform in multivariate quality control,proper orthogonal decomposition (POD) in mechanical engineering,singular value decomposition (SVD) ofX (invented in the last quarter of the 19th century[12]),eigenvalue decomposition (EVD) ofXTX in linear algebra,factor analysis (for a discussion of the differences between PCA and factor analysis see Ch. 7 of Jolliffe'sPrincipal Component Analysis),[13]Eckart–Young theorem (Harman, 1960), orempirical orthogonal functions (EOF) in meteorological science (Lorenz, 1956), empirical eigenfunction decomposition (Sirovich, 1987), quasiharmonic modes (Brooks et al., 1988),spectral decomposition in noise and vibration, andempirical modal analysis in structural dynamics.
PCA can be thought of as fitting ap-dimensionalellipsoid to the data, where each axis of the ellipsoid represents a principal component. If some axis of the ellipsoid is small, then the variance along that axis is also small.
To find the axes of the ellipsoid, we must first center the values of each variable in the dataset on 0 by subtracting the mean of the variable's observed values from each of those values. These transformed values are used instead of the original observed values for each of the variables. Then, we compute thecovariance matrix of the data and calculate the eigenvalues and corresponding eigenvectors of this covariance matrix. Then we mustnormalize each of the orthogonal eigenvectors to turn them into unit vectors. Once this is done, each of the mutually-orthogonal unit eigenvectors can be interpreted as an axis of the ellipsoid fitted to the data. This choice of basis will transform the covariance matrix into a diagonalized form, in which the diagonal elements represent the variance of each axis. The proportion of the variance that each eigenvector represents can be calculated by dividing the eigenvalue corresponding to that eigenvector by the sum of all eigenvalues.
Biplots andscree plots (degree ofexplained variance) are used to interpret findings of the PCA.
PCA is defined as anorthogonallinear transformation on a realinner product space that transforms the data to a newcoordinate system such that the greatest variance by some scalar projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on.[13]
Consider an datamatrix,X, with column-wise zeroempirical mean (the sample mean of each column has been shifted to zero), where each of then rows represents a different repetition of the experiment, and each of thep columns gives a particular kind of feature (say, the results from a particular sensor).
Mathematically, the transformation is defined by a set of size (where is usually selected to be strictly less than to reduce dimensionality) of-dimensional vectors of weights or coefficients that map each row vector ofX to a new vector of principal componentscores, given by
in such a way that the individual variables oft considered over the data set successively inherit the maximum possible variance fromX, with each coefficient vectorw constrained to be aunit vector.
The above may equivalently be written in matrix form as
where,, and.
In order to maximize variance, the first weight vectorw(1) thus has to satisfy
Equivalently, writing this in matrix form gives
Sincew(1) has been defined to be a unit vector, it equivalently also satisfies
The quantity to be maximised can be recognised as aRayleigh quotient. A standard result for apositive semidefinite matrix such asXTX is that the quotient's maximum possible value is the largesteigenvalue of the matrix, which occurs whenw is the correspondingeigenvector.
Withw(1) found, the first principal component of a data vectorx(i) can then be given as a scoret1(i) =x(i) ⋅w(1) in the transformed co-ordinates, or as the corresponding vector in the original variables, {x(i) ⋅w(1)}w(1).
Thek-th component can be found by subtracting the firstk − 1 principal components fromX:
and then finding the weight vector which extracts the maximum variance from this new data matrix
It turns out that this gives the remaining eigenvectors ofXTX, with the maximum values for the quantity in brackets given by their corresponding eigenvalues. Thus the weight vectors are eigenvectors ofXTX.
Thek-th principal component of a data vectorx(i) can therefore be given as a scoretk(i) =x(i) ⋅w(k) in the transformed coordinates, or as the corresponding vector in the space of the original variables, {x(i) ⋅w(k)}w(k), wherew(k) is thekth eigenvector ofXTX.
The full principal components decomposition ofX can therefore be given as
whereW is ap-by-p matrix of weights whose columns are the eigenvectors ofXTX. The transpose ofW is sometimes called thewhitening or sphering transformation. Columns ofW multiplied by the square root of corresponding eigenvalues, that is, eigenvectors scaled up by the variances, are calledloadings in PCA or in Factor analysis.
XTX itself can be recognized as proportional to the empirical samplecovariance matrix of the datasetXT.[13]: 30–31
The sample covarianceQ between two of the different principal components over the dataset is given by:
where the eigenvalue property ofw(k) has been used to move from line 2 to line 3. However eigenvectorsw(j) andw(k) corresponding to eigenvalues of a symmetric matrix are orthogonal (if the eigenvalues are different), or can be orthogonalised (if the vectors happen to share an equal repeated value). The product in the final line is therefore zero; there is no sample covariance between different principal components over the dataset.
Another way to characterise the principal components transformation is therefore as the transformation to coordinates which diagonalise the empirical sample covariance matrix.
In matrix form, the empirical covariance matrix for the original variables can be written
The empirical covariance matrix between the principal components becomes
whereΛ is the diagonal matrix of eigenvaluesλ(k) ofXTX.λ(k) is equal to the sum of the squares over the dataset associated with each componentk, that is,λ(k) = Σitk2(i) = Σi (x(i) ⋅w(k))2.
The transformationP =XW maps a data vectorx(i) from an original space ofx variables to a new space ofp variables which are uncorrelated over the dataset.To non-dimensionalize the centered data, letXc represent the characteristic values of data vectorsXi, given by:
for a dataset of sizen. These norms are used to transform the original space of variablesx, y to a new space of uncorrelated variablesp, q (givenYc with same meaning), such that;and the new variables are linearly related as:.To find the optimal linear relationship, we minimize the total squared reconstruction error:; such that setting the derivative of the error function to zero yields: where.[14]
Suchdimensionality reduction can be a very useful step for visualising and processing high-dimensional datasets, while still retaining as much of the variance in the dataset as possible. For example, selectingL = 2 and keeping only the first two principal components finds the two-dimensional plane through the high-dimensional dataset in which the data are most spread out, so if the data containsclusters these too may be most spread out, and therefore most visible to be plotted out in a two-dimensional diagram; whereas if two directions through the data (or two of the original variables) are chosen at random, the clusters may be much less spread apart from each other, and may in fact be much more likely to substantially overlay each other, making them indistinguishable.
Similarly, inregression analysis, the larger the number ofexplanatory variables allowed, the greater is the chance ofoverfitting the model, producing conclusions that fail to generalise to other datasets. One approach, especially when there are strong correlations between different possible explanatory variables, is to reduce them to a few principal components and then run the regression against them, a method calledprincipal component regression.
Dimensionality reduction may also be appropriate when the variables in a dataset are noisy. If each column of the dataset contains independent identically distributed Gaussian noise, then the columns ofT will also contain similarly identically distributed Gaussian noise (such a distribution is invariant under the effects of the matrixW, which can be thought of as a high-dimensional rotation of the co-ordinate axes). However, with more of the total variance concentrated in the first few principal components compared to the same noise variance, the proportionate effect of the noise is less—the first few components achieve a highersignal-to-noise ratio. PCA thus can have the effect of concentrating much of the signal into the first few principal components, which can usefully be captured by dimensionality reduction; while the later principal components may be dominated by noise, and so disposed of without great loss. If the dataset is not too large, the significance of the principal components can be tested usingparametric bootstrap, as an aid in determining how many principal components to retain.[15]
The principal components transformation can also be associated with another matrix factorization, thesingular value decomposition (SVD) ofX,
HereΣ is ann-by-prectangular diagonal matrix of positive numbersσ(k), called the singular values ofX;U is ann-by-n matrix, the columns of which are orthogonal unit vectors of lengthn called the left singular vectors ofX; andW is ap-by-p matrix whose columns are orthogonal unit vectors of lengthp and called the right singular vectors ofX.
In terms of this factorization, the matrixXTX can be written
where is the square diagonal matrix with the singular values ofXand the excess zeros chopped off that satisfies. Comparison with the eigenvector factorization ofXTX establishes that the right singular vectorsW ofX are equivalent to the eigenvectors ofXTX, while the singular valuesσ(k) of are equal to the square-root of the eigenvaluesλ(k) ofXTX.
Using the singular value decomposition the score matrixT can be written
so each column ofT is given by one of the left singular vectors ofX multiplied by the corresponding singular value. This form is also thepolar decomposition ofT.
Efficient algorithms exist to calculate the SVD ofX without having to form the matrixXTX, so computing the SVD is now the standard way to calculate a principal components analysis from a data matrix,[16] unless only a handful of components are required.
As with the eigen-decomposition, a truncatedn ×L score matrixTL can be obtained by considering only the first L largest singular values and their singular vectors:
The truncation of a matrixM orT using a truncated singular value decomposition in this way produces a truncated matrix that is the nearest possible matrix ofrankL to the original matrix, in the sense of the difference between the two having the smallest possibleFrobenius norm, a result known as theEckart–Young theorem [1936].
Theorem (Optimal k‑dimensional fit). Let P be an n×m data matrix whose columns have been mean‑centered and scaled, and let be its singular value decomposition. Then the best rank‑k approximation to P in the least‑squares (Frobenius‑norm) sense is, where Vk consists of the first k columns of V. Moreover, the relative residual variance is.
The singular values (inΣ) are the square roots of theeigenvalues of the matrixXTX. Each eigenvalue is proportional to the portion of the "variance" (more correctly of the sum of the squared distances of the points from their multidimensional mean) that is associated with each eigenvector. The sum of all the eigenvalues is equal to the sum of the squared distances of the points from their multidimensional mean. PCA essentially rotates the set of points around their mean in order to align with the principal components. This moves as much of the variance as possible (using an orthogonal transformation) into the first few dimensions. The values in the remaining dimensions, therefore, tend to be small and may be dropped with minimal loss of information (seebelow). PCA is often used in this manner fordimensionality reduction. PCA has the distinction of being the optimal orthogonal transformation for keeping the subspace that has largest "variance" (as defined above). This advantage, however, comes at the price of greater computational requirements if compared, for example, and when applicable, to thediscrete cosine transform, and in particular to the DCT-II which is simply known as the "DCT".Nonlinear dimensionality reduction techniques tend to be more computationally demanding than PCA.
PCA is sensitive to the scaling of the variables. Mathematically this sensitivity comes from the way a rescaling changes the sample‑covariance matrix that PCA diagonalises.[14]
Let be the *centered* data matrix (n rows,p columns) and define the covarianceIf the‑th variable is multiplied by a factor we obtain Hence the new covariance is
Because the eigenvalues and eigenvectors of are those of scaled by, the principal axes rotate toward any column whose variance has been inflated, exactly as the 2‑D example below illustrates.
If we have just two variables and they have the samesample variance and are completely correlated, then the PCA will entail a rotation by 45° and the "weights" (they are the cosines of rotation) for the two variables with respect to the principal component will be equal. But if we multiply all values of the first variable by 100, then the first principal component will be almost the same as that variable, with a small contribution from the other variable, whereas the second component will be almost aligned with the second original variable. This means that whenever the different variables have different units (like temperature and mass), PCA is a somewhat arbitrary method of analysis. (Different results would be obtained if one used Fahrenheit rather than Celsius for example.) Pearson's original paper was entitled "On Lines and Planes of Closest Fit to Systems of Points in Space" – "in space" implies physical Euclidean space where such concerns do not arise. One way of making the PCA less arbitrary is to use variables scaled so as to have unit variance, by standardizing the data and hence use the autocorrelation matrix instead of the autocovariance matrix as a basis for PCA. However, this compresses (or expands) the fluctuations in all dimensions of the signal space to unit variance.
Classical PCA assumes the cloud of points has already been translated so its centroid is at the origin.[14]
Write each observation as
Without subtracting we are in effect diagonalising
where is the centered matrix. The rank‑one term often dominates, forcing the leading eigenvector to point almost exactly toward the mean and obliterating any structure in the centred part. After mean subtraction that term vanishes and the principal axes align with the true directions of maximal variance.
Mean-centering is unnecessary if performing a principal components analysis on a correlation matrix, as the data are already centered after calculating correlations. Correlations are derived from the cross-product of two standard scores (Z-scores) or statistical moments (hence the name:Pearson Product-Moment Correlation). Also see the article by Kromrey & Foster-Johnson (1998) on"Mean-centering in Moderated Regression: Much Ado About Nothing". Sincecovariances are correlations of normalized variables (Z- or standard-scores) a PCA based on the correlation matrix ofX isequal to a PCA based on the covariance matrix ofZ, the standardized version ofX.
PCA is a popular primary technique inpattern recognition. It is not, however, optimized for class separability.[17] However, it has been used to quantify the distance between two or more classes by calculating center of mass for each class in principal component space and reporting Euclidean distance between center of mass of two or more classes.[18] Thelinear discriminant analysis is an alternative which is optimized for class separability.
| Symbol | Meaning | Dimensions | Indices |
|---|---|---|---|
| data matrix, consisting of the set of all data vectors, one vector per row | |||
| the number of row vectors in the data set | scalar | ||
| the number of elements in each row vector (dimension) | scalar | ||
| the number of dimensions in the dimensionally reduced subspace, | scalar | ||
| vector of empiricalmeans, one mean for each columnj of the data matrix | |||
| vector of empiricalstandard deviations, one standard deviation for each columnj of the data matrix | |||
| vector of all 1's | |||
| deviations from the mean of each columnj of the data matrix | |||
| z-scores, computed using the mean and standard deviation for each columnj of the data matrix | |||
| covariance matrix | |||
| correlation matrix | |||
| matrix consisting of the set of alleigenvectors ofC, one eigenvector per column | |||
| diagonal matrix consisting of the set of alleigenvalues ofC along itsprincipal diagonal, and 0 for all other elements ( note used above ) | |||
| matrix of basis vectors, one vector per column, where each basis vector is one of the eigenvectors ofC, and where the vectors inW are a sub-set of those inV | |||
| matrix consisting ofn row vectors, where each vector is the projection of the corresponding data vector from matrixX onto the basis vectors contained in the columns of matrixW. |
Some properties of PCA include:[13][page needed]
The statistical implication of this property is that the last few PCs are not simply unstructured left-overs after removing the important PCs. Because these last PCs have variances as small as possible they are useful in their own right. They can help to detect unsuspected near-constant linear relationships between the elements ofx, and they may also be useful inregression, in selecting a subset of variables fromx, and in outlier detection.
Before we look at its usage, we first look atdiagonal elements,
Then, perhaps the main statistical implication of the result is that not only can we decompose the combined variances of all the elements ofx into decreasing contributions due to each PC, but we can also decompose the wholecovariance matrix into contributions from each PC. Although not strictly decreasing, the elements of will tend to become smaller as increases, as is nonincreasing for increasing, whereas the elements of tend to stay about the same size because of the normalization constraints:.
As noted above, the results of PCA depend on the scaling of the variables. This can be cured by scaling each feature by its standard deviation, so that one ends up with dimensionless features with unital variance.[19]
The applicability of PCA as described above is limited by certain (tacit) assumptions[20] made in its derivation. In particular, PCA can capture linear correlations between the features but fails when this assumption is violated (see Figure 6a in the reference). In some cases, coordinate transformations can restore the linearity assumption and PCA can then be applied (seekernel PCA).
Another limitation is the mean-removal process before constructing the covariance matrix for PCA. In fields such as astronomy, all the signals are non-negative, and the mean-removal process will force the mean of some astrophysical exposures to be zero, which consequently creates unphysical negative fluxes,[21] and forward modeling has to be performed to recover the true magnitude of the signals.[22] As an alternative method,non-negative matrix factorization focusing only on the non-negative elements in the matrices is well-suited for astrophysical observations.[23][24][25] See more atthe relation between PCA and non-negative matrix factorization.
PCA is at a disadvantage if the data has not been standardized before applying the algorithm to it. PCA transforms the original data into data that is relevant to the principal components of that data, which means that the new data variables cannot be interpreted in the same ways that the originals were. They are linear interpretations of the original variables. Also, if PCA is not performed properly, there is a high likelihood of information loss.[26]
PCA relies on a linear model. If a dataset has a pattern hidden inside it that is nonlinear, then PCA can actually steer the analysis in the complete opposite direction of progress.[27][page needed] Researchers at Kansas State University discovered that the sampling error in their experiments impacted the bias of PCA results. "If the number of subjects or blocks is smaller than 30, and/or the researcher is interested in PC's beyond the first, it may be better to first correct for the serial correlation, before PCA is conducted".[28] The researchers at Kansas State also found that PCA could be "seriously biased if the autocorrelation structure of the data are not correctly handled".[28]
Dimensionality reduction results in a loss of information, in general. PCA-based dimensionality reduction tends to minimize that information loss, under certain signal and noise models.
Under the assumption that
that is, that the data vector is the sum of the desired information-bearing signal and a noise signal one can show that PCA can be optimal for dimensionality reduction, from an information-theoretic point-of-view.
In particular, Linsker showed that if is Gaussian and is Gaussian noise with a covariance matrix proportional to the identity matrix, the PCA maximizes themutual information between the desired information and the dimensionality-reduced output.[29]
If the noise is still Gaussian and has a covariance matrix proportional to the identity matrix (that is, the components of the vector areiid), but the information-bearing signal is non-Gaussian (which is a common scenario), PCA at least minimizes an upper bound on theinformation loss, which is defined as[30][31]
The optimality of PCA is also preserved if the noise is iid and at least more Gaussian (in terms of theKullback–Leibler divergence) than the information-bearing signal.[32] In general, even if the above signal model holds, PCA loses its information-theoretic optimality as soon as the noise becomes dependent.
The following is a detailed description of PCA using the covariance method[33] as opposed to the correlation method.[34]
The goal is to transform a given data setX of dimensionp to an alternative data setY of smaller dimensionL. Equivalently, we are seeking to find the matrixY, whereY is theKarhunen–Loève transform (KLT) of matrixX:
Suppose you have data comprising a set of observations ofp variables, and you want to reduce the data so that each observation can be described with onlyL variables,L <p. Suppose further, that the data are arranged as a set ofn data vectors with each representing a single grouped observation of thep variables.
Mean subtraction is an integral part of the solution towards finding a principal component basis that minimizes the mean square error of approximating the data.[35] Hence we proceed by centering the data as follows:
In some applications, each variable (column ofB) may also be scaled to have a variance equal to 1 (seeZ-score).[36] This step affects the calculated principal components, but makes them independent of the units used to measure the different variables.
LetX be ad-dimensional random vector expressed as column vector. Without loss of generality, assumeX has zero mean.
We want to find ad ×dorthonormal transformation matrixP so thatPX has a diagonal covariance matrix (that is,PX is a random vector with all its distinct components pairwise uncorrelated).
A quick computation assuming were unitary yields:
Hence holds if and only if were diagonalisable by.
This is very constructive, as cov(X) is guaranteed to be a non-negative definite matrix and thus is guaranteed to be diagonalisable by some unitary matrix.
In practical implementations, especially withhigh dimensional data (largep), the naive covariance method is rarely used because it is not efficient due to high computational and memory costs of explicitly determining the covariance matrix. The covariance-free approach avoids thenp2 operations of explicitly calculating and storing the covariance matrixXTX, instead utilizing one ofmatrix-free methods, for example, based on the function evaluating the productXT(X r) at the cost of2np operations.
One way to compute the first principal component efficiently[41] is shown in the following pseudo-code, for a data matrixX with zero mean, without ever computing its covariance matrix.
r = a random vector of lengthpr =r / norm(r)doc times:s = 0 (a vector of lengthp)for each rowx inXs =s + (x ⋅r)xλ =rTs// λ is the eigenvalueerror = |λ ⋅r −s|r =s / norm(s)exit if error < tolerancereturnλ,r
Thispower iteration algorithm simply calculates the vectorXT(X r), normalizes, and places the result back inr. The eigenvalue is approximated byrT (XTX) r, which is theRayleigh quotient on the unit vectorr for the covariance matrixXTX. If the largest singular value is well separated from the next largest one, the vectorr gets close to the first principal component ofX within the number of iterationsc, which is small relative top, at the total cost2cnp. Thepower iteration convergence can be accelerated without noticeably sacrificing the small cost per iteration using more advancedmatrix-free methods, such as theLanczos algorithm or the Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) method.
Subsequent principal components can be computed one-by-one via deflation or simultaneously as a block. In the former approach, imprecisions in already computed approximate principal components additively affect the accuracy of the subsequently computed principal components, thus increasing the error with every new computation. The latter approach in the block power method replaces single-vectorsr ands with block-vectors, matricesR andS. Every column ofR approximates one of the leading principal components, while all columns are iterated simultaneously. The main calculation is evaluation of the productXT(X R). Implemented, for example, inLOBPCG, efficient blocking eliminates the accumulation of the errors, allows using high-levelBLAS matrix-matrix product functions, and typically leads to faster convergence, compared to the single-vector one-by-one technique.
Non-linear iterative partial least squares (NIPALS) is a variant the classicalpower iteration with matrix deflation by subtraction implemented for computing the first few components in a principal component orpartial least squares analysis. For very-high-dimensional datasets, such as those generated in the *omics sciences (for example,genomics,metabolomics) it is usually only necessary to compute the first few PCs. Thenon-linear iterative partial least squares (NIPALS) algorithm updates iterative approximations to the leading scores and loadingst1 andr1T by thepower iteration multiplying on every iteration byX on the left and on the right, that is, calculation of the covariance matrix is avoided, just as in the matrix-free implementation of the power iterations toXTX, based on the function evaluating the productXT(X r) =((X r)TX)T.
The matrix deflation by subtraction is performed by subtracting the outer product,t1r1T fromX leaving the deflated residual matrix used to calculate the subsequent leading PCs.[42]For large data matrices, or matrices that have a high degree of column collinearity, NIPALS suffers from loss of orthogonality of PCs due to machine precisionround-off errors accumulated in each iteration and matrix deflation by subtraction.[43] AGram–Schmidt re-orthogonalization algorithm is applied to both the scores and the loadings at each iteration step to eliminate this loss of orthogonality.[44] NIPALS reliance on single-vector multiplications cannot take advantage of high-levelBLAS and results in slow convergence for clustered leading singular values—both these deficiencies are resolved in more sophisticated matrix-free block solvers, such as the Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) method.
In an "online" or "streaming" situation with data arriving piece by piece rather than being stored in a single batch, it is useful to make an estimate of the PCA projection that can be updated sequentially. This can be done efficiently, but requires different algorithms.[45]
In PCA, it is common that we want to introduce qualitative variables as supplementary elements. For example, many quantitative variables have been measured on plants. For these plants, some qualitative variables are available as, for example, the species to which the plant belongs. These data were subjected to PCA for quantitative variables. When analyzing the results, it is natural to connect the principal components to the qualitative variablespecies.For this, the following results are produced.
These results are what is calledintroducing a qualitative variable as supplementary element. This procedure is detailed in and Husson, Lê, & Pagès (2009) and Pagès (2013).Few software offer this option in an "automatic" way. This is the case ofSPAD that historically, following the work ofLudovic Lebart, was the first to propose this option, and the R packageFactoMineR.
The earliest application of factor analysis was in locating and measuring components of human intelligence. It was believed that intelligence had various uncorrelated components such as spatial intelligence, verbal intelligence, induction, deduction etc and that scores on these could be adduced by factor analysis from results on various tests, to give a single index known as theIntelligence Quotient (IQ). The pioneering statistical psychologistSpearman actually developed factor analysis in 1904 for histwo-factor theory of intelligence, adding a formal technique to the science ofpsychometrics. In 1924Thurstone looked for 56 factors of intelligence, developing the notion of Mental Age. Standard IQ tests today are based on this early work.[46]
In 1949, Shevky and Williams introduced the theory offactorial ecology, which dominated studies of residential differentiation from the 1950s to the 1970s.[47] Neighbourhoods in a city were recognizable or could be distinguished from one another by various characteristics which could be reduced to three by factor analysis. These were known as 'social rank' (an index of occupational status), 'familism' or family size, and 'ethnicity'; Cluster analysis could then be applied to divide the city into clusters or precincts according to values of the three key factor variables. An extensive literature developed around factorial ecology in urban geography, but the approach went out of fashion after 1980 as being methodologically primitive and having little place in postmodern geographical paradigms.
One of the problems with factor analysis has always been finding convincing names for the various artificial factors. In 2000, Flood revived the factorial ecology approach to show that principal components analysis actually gave meaningful answers directly, without resorting to factor rotation. The principal components were actually dual variables or shadow prices of 'forces' pushing people together or apart in cities. The first component was 'accessibility', the classic trade-off between demand for travel and demand for space, around which classical urban economics is based. The next two components were 'disadvantage', which keeps people of similar status in separate neighbourhoods (mediated by planning), and ethnicity, where people of similar ethnic backgrounds try to co-locate.[48]
About the same time, the Australian Bureau of Statistics defined distinct indexes of advantage and disadvantage taking the first principal component of sets of key variables that were thought to be important. These SEIFA indexes are regularly published for various jurisdictions, and are used frequently in spatial analysis.[49]
PCA can be used as a formal method for the development of indexes. As an alternativeconfirmatory composite analysis has been proposed to develop and assess indexes.[50]
The City Development Index was developed by PCA from about 200 indicators of city outcomes in a 1996 survey of 254 global cities. The first principal component was subject to iterative regression, adding the original variables singly until about 90% of its variation was accounted for. The index ultimately used about 15 indicators but was a good predictor of many more variables. Its comparative value agreed very well with a subjective assessment of the condition of each city. The coefficients on items of infrastructure were roughly proportional to the average costs of providing the underlying services, suggesting the Index was actually a measure of effective physical and social investment in the city.
The country-levelHuman Development Index (HDI) fromUNDP, which has been published since 1990 and is very extensively used in development studies,[51] has very similar coefficients on similar indicators, strongly suggesting it was originally constructed using PCA.
In 1978Cavalli-Sforza and others pioneered the use of principal components analysis (PCA) to summarise data on variation in human gene frequencies across regions. The components showed distinctive patterns, including gradients and sinusoidal waves. They interpreted these patterns as resulting from specific ancient migration events.
Since then, PCA has been ubiquitous in population genetics, with thousands of papers using PCA as a display mechanism. Genetics varies largely according to proximity, so the first two principal components actually show spatial distribution and may be used to map the relative geographical location of different population groups, thereby showing individuals who have wandered from their original locations.[52]
PCA in genetics has been technically controversial, in that the technique has been performed on discrete non-normal variables and often on binary allele markers. The lack of any measures of standard error in PCA are also an impediment to more consistent usage. In August 2022, the molecular biologistEran Elhaik published a theoretical paper inScientific Reports analyzing 12 PCA applications. He concluded that it was easy to manipulate the method, which, in his view, generated results that were 'erroneous, contradictory, and absurd.' Specifically, he argued, the results achieved in population genetics were characterized by cherry-picking andcircular reasoning.[53]
Market research has been an extensive user of PCA. It is used to develop customer satisfaction or customer loyalty scores for products, and with clustering, to develop market segments that may be targeted with advertising campaigns, in much the same way as factorial ecology will locate geographical areas with similar characteristics.[54]
PCA rapidly transforms large amounts of data into smaller, easier-to-digest variables that can be more rapidly and readily analyzed. In any consumer questionnaire, there are series of questions designed to elicit consumer attitudes, and principal components seek out latent variables underlying these attitudes. For example, the Oxford Internet Survey in 2013 asked 2000 people about their attitudes and beliefs, and from these analysts extracted four principal component dimensions, which they identified as 'escape', 'social networking', 'efficiency', and 'problem creating'.[55]
Another example from Joe Flood in 2008 extracted an attitudinal index toward housing from 28 attitude questions in a national survey of 2697 households in Australia. The first principal component represented a general attitude toward property and home ownership. The index, or the attitude questions it embodied, could be fed into a General Linear Model of tenure choice. The strongest determinant of private renting by far was the attitude index, rather than income, marital status or household type.[56]
Inquantitative finance, PCA is used[57]infinancial risk management, and has been applied toother problems such asportfolio optimization.
PCA is commonly used in problems involvingfixed income securities andportfolios, andinterest rate derivatives. Valuations here depend on the entireyield curve, comprising numerous highly correlated instruments, and PCA is used to define a set of components or factors that explain rate movements,[58]thereby facilitating the modelling.One common risk management application is tocalculating value at risk, VaR, applying PCA to theMonte Carlo simulation.[59]Here, for each simulation-sample, the components are stressed, and rates, andin turn option values, are then reconstructed; with VaR calculated, finally, over the entire run. PCA is also used inhedging exposure tointerest rate risk, givenpartial durations and other sensitivities.[58]Under both, the first three, typically, principal components of the system are of interest (representing "shift", "twist", and "curvature").These principal components are derived from an eigen-decomposition of thecovariance matrix ofyield at predefined maturities;[60]and where thevariance of each component is itseigenvalue (and as the components areorthogonal, no correlation need be incorporated in subsequent modelling).
Forequity, an optimal portfolio is one where theexpected return is maximized for a given level of risk, or alternatively, where risk is minimized for a given return; seeMarkowitz model for discussion.Thus, one approach is to reduce portfolio risk, whereallocation strategies are applied to the "principal portfolios" instead of the underlyingstocks.A second approach is to enhance portfolio return, using the principal components to select companies' stocks with upside potential.[61][62]PCA has also been used to understand relationships[57] between internationalequity markets, and within markets between groups of companies in industries orsectors.
PCA may also be applied tostress testing,[63] essentially an analysis of a bank's ability to endurea hypothetical adverse economic scenario. Its utility is in "distilling the information contained in [several]macroeconomic variables into a more manageable data set, which can then [be used] for analysis."[63] Here, the resulting factors are linked to e.g. interest rates – based on the largest elements of the factor'seigenvector – and it is then observed how a "shock" to each of the factors affects the implied assets of each of the banks.
A variant of principal components analysis is used inneuroscience to identify the specific properties of a stimulus that increases aneuron's probability of generating anaction potential.[64][65] This technique is known asspike-triggered covariance analysis. In a typical application an experimenter presents awhite noise process as a stimulus (usually either as a sensory input to a test subject, or as acurrent injected directly into the neuron) and records a train of action potentials, or spikes, produced by the neuron as a result. Presumably, certain features of the stimulus make the neuron more likely to spike. In order to extract these features, the experimenter calculates thecovariance matrix of thespike-triggered ensemble, the set of all stimuli (defined and discretized over a finite time window, typically on the order of 100 ms) that immediately preceded a spike. Theeigenvectors of the difference between the spike-triggered covariance matrix and the covariance matrix of theprior stimulus ensemble (the set of all stimuli, defined over the same length time window) then indicate the directions in thespace of stimuli along which the variance of the spike-triggered ensemble differed the most from that of the prior stimulus ensemble. Specifically, the eigenvectors with the largest positive eigenvalues correspond to the directions along which the variance of the spike-triggered ensemble showed the largest positive change compared to the variance of the prior. Since these were the directions in which varying the stimulus led to a spike, they are often good approximations of the sought after relevant stimulus features.
In neuroscience, PCA is also used to discern the identity of a neuron from the shape of its action potential.Spike sorting is an important procedure becauseextracellular recording techniques often pick up signals from more than one neuron. In spike sorting, one first uses PCA to reduce the dimensionality of the space of action potential waveforms, and then performsclustering analysis to associate specific action potentials with individual neurons.
PCA as a dimension reduction technique is particularly suited to detect coordinated activities of large neuronal ensembles. It has been used in determining collective variables, that is,order parameters, duringphase transitions in the brain.[66]
Correspondence analysis (CA)was developed byJean-Paul Benzécri[67]and is conceptually similar to PCA, but scales the data (which should be non-negative) so that rows and columns are treated equivalently. It is traditionally applied tocontingency tables.CA decomposes thechi-squared statistic associated to this table into orthogonal factors.[68]Because CA is a descriptive technique, it can be applied to tables for which the chi-squared statistic is appropriate or not.Several variants of CA are available includingdetrended correspondence analysis andcanonical correspondence analysis. One special extension ismultiple correspondence analysis, which may be seen as the counterpart of principal component analysis for categorical data.[69]
Principal component analysis creates variables that are linear combinations of the original variables. The new variables have the property that the variables are all orthogonal. The PCA transformation can be helpful as a pre-processing step before clustering. PCA is a variance-focused approach seeking to reproduce the total variable variance, in which components reflect both common and unique variance of the variable. PCA is generally preferred for purposes of data reduction (that is, translating variable space into optimal factor space) but not when the goal is to detect the latent construct or factors.
Factor analysis is similar to principal component analysis, in that factor analysis also involves linear combinations of variables. Different from PCA, factor analysis is a correlation-focused approach seeking to reproduce the inter-correlations among variables, in which the factors "represent the common variance of variables, excluding unique variance".[70] In terms of the correlation matrix, this corresponds with focusing on explaining the off-diagonal terms (that is, shared co-variance), while PCA focuses on explaining the terms that sit on the diagonal. However, as a side result, when trying to reproduce the on-diagonal terms, PCA also tends to fit relatively well the off-diagonal correlations.[13]: 158 Results given by PCA and factor analysis are very similar in most situations, but this is not always the case, and there are some problems where the results are significantly different. Factor analysis is generally used when the research purpose is detecting data structure (that is, latent constructs or factors) orcausal modeling. If the factor model is incorrectly formulated or the assumptions are not met, then factor analysis will give erroneous results.[71]
It has been asserted that the relaxed solution ofk-means clustering, specified by the cluster indicators, is given by the principal components, and the PCA subspace spanned by the principal directions is identical to the cluster centroid subspace.[72][73] However, that PCA is a useful relaxation ofk-means clustering was not a new result,[74] and it is straightforward to uncover counterexamples to the statement that the cluster centroid subspace is spanned by the principal directions.[75]
Non-negative matrix factorization (NMF) is a dimension reduction method where only non-negative elements in the matrices are used, which is therefore a promising method in astronomy,[23][24][25] in the sense that astrophysical signals are non-negative. The PCA components are orthogonal to each other, while the NMF components are all non-negative and therefore constructs a non-orthogonal basis.
In PCA, the contribution of each component is ranked based on the magnitude of its corresponding eigenvalue, which is equivalent to the fractional residual variance (FRV) in analyzing empirical data.[21] For NMF, its components are ranked based only on the empirical FRV curves.[25] The residual fractional eigenvalue plots, that is, as a function of component number given a total of components, for PCA have a flat plateau, where no data are captured to remove the quasi-static noise, then the curves drop quickly as an indication of over-fitting (random noise).[21] The FRV curves for NMF is decreasing continuously[25] when the NMF components are constructedsequentially,[24] indicating the continuous capturing of quasi-static noise; then converge to higher levels than PCA,[25] indicating the less over-fitting property of NMF.
It is often difficult to interpret the principal components when the data include many variables of various origins, or when some variables are qualitative. This leads the PCA user to a delicate elimination of several variables. If observations or variables have an excessive impact on the direction of the axes, they should be removed and then projected as supplementary elements. In addition, it is necessary to avoid interpreting the proximities between the points close to the center of the factorial plane.
Theiconography of correlations, on the contrary, which is not a projection on a system of axes, does not have these drawbacks. We can therefore keep all the variables.
The principle of the diagram is to underline the "remarkable" correlations of the correlation matrix, by a solid line (positive correlation) or dotted line (negative correlation).
A strong correlation is not "remarkable" if it is not direct, but caused by the effect of a third variable. Conversely, weak correlations can be "remarkable". For example, if a variable Y depends on several independent variables, the correlations of Y with each of them are weak and yet "remarkable".
A particular disadvantage of PCA is that the principal components are usually linear combinations of all input variables.Sparse PCA overcomes this disadvantage by finding linear combinations that contain just a few input variables. It extends the classic method of principal component analysis (PCA) for the reduction of dimensionality of data by adding sparsity constraint on the input variables.Several approaches have been proposed, including
The methodological and theoretical developments of Sparse PCA as well as its applications in scientific studies were recently reviewed in a survey paper.[82]
Most of the modern methods fornonlinear dimensionality reduction find their theoretical and algorithmic roots in PCA or K-means. Pearson's original idea was to take a straight line (or plane) which will be "the best fit" to a set of data points.Trevor Hastie expanded on this concept by proposingPrincipalcurves[86] as the natural extension for the geometric interpretation of PCA, which explicitly constructs a manifold for dataapproximation followed byprojecting the points onto it. See also theelastic map algorithm andprincipal geodesic analysis.[87] Another popular generalization iskernel PCA, which corresponds to PCA performed in a reproducing kernel Hilbert space associated with a positive definite kernel.
Inmultilinear subspace learning,[88][89][90] PCA is generalized tomultilinear PCA (MPCA) that extracts features directly from tensor representations. MPCA is solved by performing PCA in each mode of the tensor iteratively. MPCA has been applied to face recognition, gait recognition, etc. MPCA is further extended to uncorrelated MPCA, non-negative MPCA and robust MPCA.
N-way principal component analysis may be performed with models such asTucker decomposition,PARAFAC, multiple factor analysis, co-inertia analysis, STATIS, and DISTATIS.
While PCA finds the mathematically optimal method (as in minimizing the squared error), it is still sensitive tooutliers in the data that produce large errors, something that the method tries to avoid in the first place. It is therefore common practice to remove outliers before computing PCA. However, in some contexts, outliers can be difficult to identify.[91]For example, indata mining algorithms likecorrelation clustering, the assignment of points to clusters and outliers is not known beforehand.A recently proposed generalization of PCA[92] based on a weighted PCA increases robustness by assigning different weights to data objects based on their estimated relevancy.
Outlier-resistant variants of PCA have also been proposed, based on L1-norm formulations (L1-PCA).[7][5]
Robust principal component analysis (RPCA) via decomposition in low-rank and sparse matrices is a modification of PCA that works well with respect to grossly corrupted observations.[93][94][95]
Independent component analysis (ICA) is directed to similar problems as principal component analysis, but finds additively separable components rather than successive approximations.
Given a matrix, it tries to decompose it into two matrices such that. A key difference from techniques such as PCA and ICA is that some of the entries of are constrained to be 0. Here is termed the regulatory layer. While in general such a decomposition can have multiple solutions, they prove that if the following conditions are satisfied :
then the decomposition is unique up to multiplication by a scalar.[96]
Discriminant analysis of principal components (DAPC) is a multivariate method used to identify and describe clusters of genetically related individuals. Genetic variation is partitioned into two components: variation between groups and within groups, and it maximizes the former. Linear discriminants are linear combinations of alleles which best separate the clusters. Alleles that most contribute to this discrimination are therefore those that are the most markedly different across groups. The contributions of alleles to the groupings identified by DAPC can allow identifying regions of the genome driving the genetic divergence among groups[97]In DAPC, data are first transformed using a principal components analysis (PCA) and subsequently clusters are identified using discriminant analysis (DA).
A DAPC can be realized on R using the package Adegenet. (more info:adegenet on the web)
Directional component analysis (DCA) is a method used in the atmospheric sciences for analysing multivariate datasets.[98]Like PCA, it allows for dimension reduction, improved visualization and improved interpretability of large data-sets.Also like PCA, it is based on a covariance matrix derived from the input dataset.The difference between PCA and DCA is that DCA additionally requires the input of a vector direction, referred to as the impact. Whereas PCA maximises explained variance, DCA maximises probability density given impact.The motivation for DCA is to find components of a multivariate dataset that are both likely (measured using probability density) and important (measured using the impact).DCA has been used to find the most likely and most serious heat-wave patterns in weather prediction ensembles,[99] and the most likely and most impactful changes in rainfall due to climate change.[100]
pca command or via theprincomp() function.pca function in the MultivariateStats package.princomp andpca (R2012b) give the principal components, while the functionpcares gives the residuals and reconstructed matrix for a low-rank PCA approximation.g03aa routine (available in both the Fortran versions of the Library).princomp gives the principal component.DBMS_DATA_MINING.SVDS_SCORING_MODE by specifying setting valueSVDS_SCORING_PCA.princomp andprcomp can be used for principal component analysis;prcomp usessingular value decomposition which generally gives better numerical accuracy. Some packages that implement PCA in R, include, but are not limited to:ade4,vegan,ExPosition,dimRed, andFactoMineR.princomp computes principal component analysis, the functionpca computes principal component analysis with standardized variables.{{cite journal}}: CS1 maint: multiple names: authors list (link){{cite conference}}: CS1 maint: multiple names: authors list (link)
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