Improving principal component analysis using Bayesian estimation

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

doi:10.1109/acc.2001.946204

Improving principal component analysis using Bayesian estimation

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

Statistics (Twin Cities)

Research output: Contribution to journal › Conference article › peer-review

4 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 language	English (US)
Pages (from-to)	3666-3671
Number of pages	6
Journal	Proceedings of the American Control Conference
Volume	5
DOIs	https://doi.org/10.1109/acc.2001.946204
State	Published - 2001
Event	2001 American Control Conference - Arlington, VA, United States Duration: Jun 25 2001 → Jun 27 2001

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title = "Improving principal component analysis using Bayesian estimation",

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.",

author = "Nounou, {M. N.} and Bakshi, {B. R.} and Goel, {P. K.} and X. Shen",

year = "2001",

doi = "10.1109/acc.2001.946204",

language = "English (US)",

volume = "5",

pages = "3666--3671",

journal = "Proceedings of the American Control Conference",

issn = "0743-1619",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "2001 American Control Conference ; Conference date: 25-06-2001 Through 27-06-2001",

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AU - Nounou, M. N.

AU - Bakshi, B. R.

AU - Goel, P. K.

AU - Shen, X.

PY - 2001

Y1 - 2001

N2 - 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.

AB - 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.

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U2 - 10.1109/acc.2001.946204

DO - 10.1109/acc.2001.946204

M3 - Conference article

AN - SCOPUS:0034848185

SN - 0743-1619

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EP - 3671

JO - Proceedings of the American Control Conference

JF - Proceedings of the American Control Conference

T2 - 2001 American Control Conference

Y2 - 25 June 2001 through 27 June 2001

ER -

Improving principal component analysis using Bayesian estimation

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