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.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Paradis, Emmanuel: Reduced multidimensional scaling (2022)
- N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz: Randomized Matrix Decompositions Using R (2019) not zbMATH
- Alfonso Iodice D’Enza, Angelos Markos, Davide Buttarazzi: The idm Package: Incremental Decomposition Methods in R (2018) not zbMATH
- Herve Cardot, David Degras: Online Principal Component Analysis in High Dimension: Which Algorithm to Choose? (2015) arXiv