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titleAutomatic choice of dimensionality for PCA
authorsThomas Minka
keywordsprincipal component analysis, PCA,
abstractA central issue in principal component analysis (PCA) is choosing the number of principal components to be retained. By interpreting PCA as density estimation, we show how to use Bayesian model selection to estimate the true dimensionality of the data. The resulting estimate is simple
to compute yet guaranteed to pick the correct dimensionality, given enough data.
typeProceeding
journalProceedings of NIPS, 2000
published year
serial1234
is_viewableyes
(Total records:1429)
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