## TR#514: Automatic choice of dimensionality for PCA

### Thomas P. Minka

Appears in NIPS 13

(revised 12/29/00)
A central issue in principal component analysis (PCA) is choosing the
number of principal components to be retained. By interpreting PCA as
density estimation, this paper shows how to use Bayesian model selection to
determine the true dimensionality of the data. The resulting estimate is
simple to compute yet guaranteed to pick the correct dimensionality, given
enough data. In simulations, it is more accurate than cross-validation and
other proposed algorithms, plus it runs much faster.

Postscript

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