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Title: Bayesian inference on principal component analysis using reversible jump Markov chain Monte Carlo
Authors: Zhang, Zhihua
Chan, Kap Luk
Kwok, James Tin-Yau
Yeung, Dit-Yan
Keywords: Principal component analysis (PCA)
Markov chain Monte Carlo (MCMC)
Bayesian inference
Issue Date: 2004
Citation: Proceedings of the 19th National Conference on Artificial Intelligence, San Jose, California, USA, 25-29 July 2004, AAAI Press, Menlo Park, California, p.372-377
Abstract: Based on the probabilistic reformulation of principal component analysis (PCA), we consider the problem of determining the number of principal components as a model selection problem. We present a hierarchical model for probabilistic PCA and construct a Bayesian inference method for this model using reversible jump Markov chain Monte Carlo (MCMC). By regarding each principal component as a point in a one-dimensional space and employing only birthdeath moves in our reversible jump methodology, our proposed method is simple and capable of automatically determining the number of principal components and estimating the parameters simultaneously under the same disciplined framework. Simulation experiments are performed to demonstrate the effectiveness of our MCMC method.
Rights: Copyright © 2004 American Association for Artificial Intelligence. Information about AAAI publications is available at:
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