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arXiv · 1007.5510

An algorithm for the principal component analysis of large data sets

Nathan Halko·Per-Gunnar Martinsson·Yoel Shkolnisky·Mark Tygert
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Abstract

Recently popularized randomized methods for principal component analysis (PCA) efficiently and reliably produce nearly optimal accuracy --- even on parallel processors --- unlike the classical (deterministic) alternatives. We adapt one of these randomized methods for use with data sets that are too large to be stored in random-access memory (RAM). (The traditional terminology is that our procedure works efficiently "out-of-core.") We illustrate the performance of the algorithm via several numerical examples. For example, we report on the PCA of a data set stored on disk that is so large that less than a hundredth of it can fit in our computer's RAM.

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BibTeXRIS

Nathan Halko, Per-Gunnar Martinsson, Yoel Shkolnisky, Mark Tygert. 2010-07-30. An algorithm for the principal component analysis of large data sets. https://arxiv.org/abs/1007.5510

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