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An algorithm for the principal component analysis of large data sets

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(2010)cite arxiv:1007.5510Comment: 17 pages, 3 figures (each with 2 or 3 subfigures), 2 tables (each with 2 subtables).

Аннотация

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 öut-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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