Improving Principal Component Analysis using Bayesian estimation Conference Paper

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.

name of conference

• Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148)

authors

• Nounou, Mohamed

published proceedings

• PROCEEDINGS OF THE 2001 AMERICAN CONTROL CONFERENCE, VOLS 1-6

author list (cited authors)

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

citation count

• 3

complete list of authors

• Nounou, MN||Bakshi, BR||Goel, PK||Shen, XT

publication date

• January 2001

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Proceedings of the ... American Control Conference. American Control Conference  Journal

Digital Object Identifier (DOI)

• 10.1109/acc.2001.946204

International Standard Book Number (ISBN) 10

• 0-7803-6495-3

start page

• 3666

end page

• 3671

volume

• 5

URL

• http://dx.doi.org/10.1109/acc.2001.946204