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2010

An algorithm for the principal component analysis of large data sets

Halko, Nathan, Martinsson, Per-Gunnar, Shkolnisky, Yoel et al.

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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.

An algorithm for the principal component analysis of large data sets · Around

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