swMATH ID: 21315
Software Authors: Hervé Cardot, David Degras
Description: R package onlinePCA: Online Principal Component Analysis: Online Principal Component Analysis in High Dimension: Which Algorithm to Choose? In the current context of data explosion, online techniques that do not require storing all data in memory are indispensable to routinely perform tasks like principal component analysis (PCA). Recursive algorithms that update the PCA with each new observation have been studied in various fields of research and found wide applications in industrial monitoring, computer vision, astronomy, and latent semantic indexing, among others. This work provides guidance for selecting an online PCA algorithm in practice. We present the main approaches to online PCA, namely, perturbation techniques, incremental methods, and stochastic optimization, and compare their statistical accuracy, computation time, and memory requirements using artificial and real data. Extensions to missing data and to functional data are discussed. All studied algorithms are available in the R package onlinePCA on CRAN.
Homepage: https://cran.r-project.org/web/packages/onlinePCA/index.html
Source Code: https://github.com/cran/onlinePCA
Dependencies: R
Keywords: Machine Learning; arXiv stat.ML; Learning; arXiv cs.LG; Methodology; arXiv stat.ME; arXiv; R package; Covariance matrix; Eigendecomposition; Generalized hebbian algorithm; Incremental SVD; Perturbation methods; Recursive algorithms; Stochastic gradient
Related Software: R; ARPACK; irlba; idm; ade4; FactoMineR; caret; svd; RSpectra; rsvd; TRLan; animation; gmodels; ade4TkGUI; corpcor; rARPACK; vegan; poLCA; twitteR; UCI-ml
Cited in: 1 Publication

Cited by 1 Author

1 Paradis, Emmanuel

Cited in 1 Serial

1 Computational Statistics

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