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Chan K, Lee TW, Sejnowski TJ. Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components. J Mach Learn Res. 2002;3:99-114
Chan, K., Lee, T. W., & Sejnowski, T. J. (2002). Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components. Journal of machine learning research : JMLR, 399-114.
Chan, Kwokleung, et al. "Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components." Journal of machine learning research : JMLR vol. 3 (2002): 99-114.
Chan K, Lee TW, Sejnowski TJ. Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components. J Mach Learn Res. 2002 Aug 01;3:99-114. PMID: 21479123; PMCID: PMC3072251.
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J Mach Learn Res. 2002 Aug 01;3:99-114.

Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components.

Journal of machine learning research : JMLR

Kwokleung Chan, Te-Won Lee, Terrence J Sejnowski

Affiliations

  1. Computational Neurobiology Laboratory, The Salk Institute, 10010 North Torrey Pines Road, La Jolla, CA 92037, USA.

PMID: 21479123 PMCID: PMC3072251

Abstract

We apply a variational method to automatically determine the number of mixtures of independent components in high-dimensional datasets, in which the sources may be nonsymmetrically distributed. The data are modeled by clusters where each cluster is described as a linear mixture of independent factors. The variational Bayesian method yields an accurate density model for the observed data without overfitting problems. This allows the dimensionality of the data to be identified for each cluster. The new method was successfully applied to a difficult real-world medical dataset for diagnosing glaucoma.

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