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PLFD: Portmanteau Local Feature Discrimination for Matrix-Variate Data

Description

The portmanteau local feature discriminant approach first identifies the local discriminant features and their differential structures, then constructs the discriminant rule by pooling the identified local features together. This method is applicable to high-dimensional matrix-variate data. See the paper by Xu, Luo and Chen (2023,doi:10.1007/s13171-021-00255-2).

Author(s)

Maintainer: Zengchao Xuzengc.xu@aliyun.com

Authors:

• Shan Luo

• Zehua Chen

See Also

Useful links:

• https://github.com/paradoxical-rhapsody/PLFD

• Report bugs athttps://github.com/paradoxical-rhapsody/PLFD/issues

PLFD

Description

A portmanteau local feature discrimination (PLFD) approach to the classification withhigh-dimensional matrix-variate data.

Usage

plfd(x, y, r0, c0, blockList, blockMode = NULL, permNum = 100, alpha = 0)

Arguments

Details

There are two ways to specify the blocks under consideration. In the case thatthe matrix-variate is partition into non-overlapping blocks that share the commonrow size and column size, these sizes can be specified byr0 andc0. Otherwise, theblocks can be flexibly specified by parameterblockList, which should be a list inwhich each element includesrIdx andcIdx corresponding to the row and column indexset of a block. See examples.

Value

List.

• n1,n2,rDim,cDim,blockMode,permNum,alpha;

• blockNumber: the number of identified feature blocks.

• paras:list(list(rIdx, cIdx, B, M), ...), list of the information offeature blocks.

References

Xu Z., Luo S. and Chen Z. (2021). A Portmanteau Local Feature DiscriminationApproach to the Classification with High-dimensional Matrix-variate Data. Sankhya A.doi:10.1007/s13171-021-00255-2

Examples

set.seed(2023)rDim <- 20cDim <- 20n <- 100y <- sample(1:2, n, TRUE, c(0.5, 0.5))x <- array(rnorm(rDim*cDim*n), dim=c(rDim, cDim, n))x[, , y==2] <- (x[, , y==2] + 1.0)ntest <- 200ytest <- sample(1:2, ntest, TRUE, c(0.5, 0.5))xtest <- array(rnorm(rDim*cDim*ntest), dim=c(rDim, cDim, ntest))xtest[, , ytest==2] <- (xtest[, , ytest==2] + 1.0)## Uniform partitionprint( plfd(x, y, r0=5, c0=5) )## Pre-specify feature blocksblockList <- list(list(rIdx=1:5, cIdx=1:5),                   list(rIdx=6:10, cIdx=1:5),                   list(rIdx=3:9, cIdx=2:8))print( plfd.model <- plfd(x, y, blockList=blockList) )## Predictpredict(plfd.model, xtest, ytest)

Predict Method forplfd

Description

Predict Method forplfd

Usage

## S3 method for class 'plfd'predict(object, x, y, ...)

Arguments

Value

list(W, y.hat, mcr) with

• W: discriminant scores;

• y.hat: predicted labels;

• mcr: misclassification rate if parametery is available.

Print Method forplfd

Description

Print Method forplfd

Usage

## S3 method for class 'plfd'print(x, ...)

Arguments