期刊
SIAM JOURNAL ON SCIENTIFIC COMPUTING
卷 33, 期 5, 页码 2580-2594出版社
SIAM PUBLICATIONS
DOI: 10.1137/100804139
关键词
algorithm; principal component analysis; PCA; singular value decomposition; SVD; low rank
资金
- NSF [DMS0748488, DMS0610097]
- Israel Science Foundation [485/10]
- Alfred P. Sloan Research Fellowship
- mathematics departments of UCLA and Yale
- DOD Counterdrug Technology Development Program Office
- Division Of Mathematical Sciences [0748488, 0941476] Funding Source: National Science Foundation
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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