Abstract
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.
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© 2002 Springer-Verlag Berlin Heidelberg
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Liu, Z., Xu, L. (2002). On Convergence of an Iterative Factor Estimate Algorithm for the NFA Model. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_166
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DOI: https://doi.org/10.1007/3-540-46084-5_166
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