Improving principal component analysis using Bayesian estimation

M. N. Nounou, B. R. Bakshi, P. K. Goel, X. Shen

Research output: Contribution to journalConference article

3 Scopus citations

Abstract

Bayesian estimation is used in this paper to derive a new PCA modeling algorithm that improves the estimation accuracy by incorporating prior knowledge about the data and model. It is shown that the algorithm is more general than existing methods, PCA and MLPCA, and reduces to these techniques when a uniform prior is used. It is also shown that when no external information is available, an empirically estimated prior from the available data can still provide improved accuracy over non-Bayesian methods.

Original languageEnglish (US)
Pages (from-to)3666-3671
Number of pages6
JournalProceedings of the American Control Conference
Volume5
DOIs
StatePublished - 2001
Event2001 American Control Conference - Arlington, VA, United States
Duration: Jun 25 2001Jun 27 2001

Fingerprint Dive into the research topics of 'Improving principal component analysis using Bayesian estimation'. Together they form a unique fingerprint.

  • Cite this