Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components
Kwokleung Chan, Te-Won Lee, Terrence J. Sejnowski;
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.