Principal Components
Principal Components is a statistical construct that represents the orthogonal directions of maximum variance in a dataset, derived through linear transformation to reduce dimensionality while preserving the greatest amount of information.
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Parents
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Matthew Turk, Alex Pentland · Journal of Cognitive Neuroscience · 1991 · 13.8K citations · Full text
Svante Wold, Kim H. Esbensen, Paul Geladi · Chemometrics and Intelligent Laboratory Systems · 1987 · 11.5K citations
Principal components analysis corrects for stratification in genome-wide association studies
Alkes L. Price, Nick J. Patterson, Robert M. Plenge et al. · Nature Genetics · 2006 · 10.7K citations
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Stanford University
Stanford, United States
47 h-index
141 publications
57.9K citations
University of California, Davis
Davis, United States
46 h-index
169 publications
16.7K citations
Harvard University
Cambridge, United States
44 h-index
187 publications
47.5K citations
University of Washington
Seattle, United States
44 h-index
178 publications
16K citations

Centre National de la Recherche Scientifique
Paris, France
43 h-index
228 publications
11.4K citations

Chinese Academy of Sciences
Beijing, China
42 h-index
285 publications
15.4K citations
University of Michigan
Ann Arbor, United States
41 h-index
165 publications
11.1K citations

University of Minnesota
Minneapolis, United States
40 h-index
145 publications
16.5K citations
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