Probabilistic Principal Component Analysis

Clicks: 3
ID: 289810
1999
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Ranked #124 of 145 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)

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Abstract
Summary Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based on a probability model. We demonstrate how the principal axes of a set of observed data vectors may be determined through maximum likelihood estimation of parameters in a latent variable model that is closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss, with illustrative examples, the advantages conveyed by this probabilistic approach to PCA.
Reference Key
openalex_W2125027820 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Michael E. Tipping, Chris Bishop
Journal Journal of the Royal Statistical Society Series B (Statistical Methodology)
Year 1999
DOI
10.1111/1467-9868.00196
URL
https://academic.oup.com/jrsssb/article-pdf/61/3/611/49589634/jrsssb_61_3_611.pdf
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