Computer Science > Machine Learning
arXiv:2406.03396 (cs)
[Submitted on 5 Jun 2024]
Title:Noisy Data Visualization using Functional Data Analysis
View a PDF of the paper titled Noisy Data Visualization using Functional Data Analysis, by Haozhe Chen and 3 other authors
View PDFHTML (experimental)Abstract:Data visualization via dimensionality reduction is an important tool in exploratory data analysis. However, when the data are noisy, many existing methods fail to capture the underlying structure of the data. The method called Empirical Intrinsic Geometry (EIG) was previously proposed for performing dimensionality reduction on high dimensional dynamical processes while theoretically eliminating all noise. However, implementing EIG in practice requires the construction of high-dimensional histograms, which suffer from the curse of dimensionality. Here we propose a new data visualization method called Functional Information Geometry (FIG) for dynamical processes that adapts the EIG framework while using approaches from functional data analysis to mitigate the curse of dimensionality. We experimentally demonstrate that the resulting method outperforms a variant of EIG designed for visualization in terms of capturing the true structure, hyperparameter robustness, and computational speed. We then use our method to visualize EEG brain measurements of sleep activity.
| Subjects: | Machine Learning (cs.LG); Functional Analysis (math.FA); Machine Learning (stat.ML) |
| Cite as: | arXiv:2406.03396 [cs.LG] |
| (orarXiv:2406.03396v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2406.03396 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Noisy Data Visualization using Functional Data Analysis, by Haozhe Chen and 3 other authors
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