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Bayesian inference on principal component analysis using reversible jump markov chain Monte Carlo

Authors Zhang, Z.
Chan, K.L.
Kwok, J.T.
Yeung, D.-Y.
Issue Date 2004
Source Proceedings of the National Conference on Artificial Intelligence , 2004, p. 372-377
Summary 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 birth-death 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.
ISBN 0-262-51183-5
Rights Copyright © 2004 American Association for Artificial Intelligence. Information about AAAI publications is available at:
Language English
Format Conference paper
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