The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
An Algorithm for the Principal Component Analysis of Large Data Sets
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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Indexing by latent semantic analysis
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Phillips, P. J., Moon, H., Rizvi, S. A., & Rauss, P. J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.879790 · Cited by 3,692
The FERET database and evaluation procedure for face-recognition algorithms
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Randomized algorithms for the low-rank approximation of matrices
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A Randomized Algorithm for Principal Component Analysis
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Self-citationA fast randomized algorithm for the approximation of matrices
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Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia
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Checinska Sielaff, A., Urbaniak, C., Mohan, G. B. M., Stepanov, V. G., Tran, Q., Wood, J. M., Minich, J., McDonald, D., Mayer, T., Knight, R., et al. (2019) · Microbiome · doi:10.1186/s40168-019-0666-x · Cited by 267
Metadata
- Authors
- Halko, N., Martinsson, P.-G., Shkolnisky, Y., & Tygert, M.
- Venue
- SIAM Journal on Scientific Computing
- Published
- 2011-01
- Volume
- 33
- Issue
- 5
- Pages
- 2580-2594
- ISSN
- 1064-8275 (ISSN-L)
- 1095-7197
- OpenAlex
- W1505090736
- Cited by
- 291
- References
- 13
- FWCI
- 11.06
- Type
- journal article
- Language
- en
- Open access
- closed
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