Neural Computation
An Introduction to Variational Methods for Graphical Models
Machine Learning
Natural gradient learning for over- and under-complete bases in ICA
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Mean-field approaches to independent component analysis
Neural Computation
A Constrained EM Algorithm for Independent Component Analysis
Neural Computation
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Variational Bayesian learning of ICA with missing data
Neural Computation
On the Effect of the Form of the Posterior Approximation in Variational Learning of ICA Models
Neural Processing Letters
A general procedure for learning mixtures of independent component analyzers
Pattern Recognition
Super-Gaussian mixture source model for ICA
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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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.