Improving principal component analysis using Bayesian estimation

Mohamed Nounou, B. R. Bakshi, P. K. Goel, X. Shen

Research output: Contribution to journalConference article

3 Citations (Scopus)

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
Pages (from-to)3666-3671
Number of pages6
JournalProceedings of the American Control Conference
Volume5
Publication statusPublished - 1 Jan 2001
Externally publishedYes

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Principal component analysis

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Improving principal component analysis using Bayesian estimation. / Nounou, Mohamed; Bakshi, B. R.; Goel, P. K.; Shen, X.

In: Proceedings of the American Control Conference, Vol. 5, 01.01.2001, p. 3666-3671.

Research output: Contribution to journalConference article

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