Neural Computation
A Comparative Study on Three MAP Factor Estimate Approaches for NFA
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
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Neural Computation
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A Comparative Study on Three MAP Factor Estimate Approaches for NFA
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
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The iterative fixed posteriori approximation (iterative FPA) has been empirically shown to be an efficient approach for the MAP factor estimate in the Non-Gaussian Factor Analysis (NFA) model. In this paper we further prove that it is exactly an EM algorithm for the MAP factor estimate problem. Thus its convergence can be guaranteed. We also empirically show that NFA has better generalization ability than Independent Factor Analysis (IFA) on data with small sample size.