Applying Mutual Information to Adaptive Mixture Models

Authors:
Zheng Rong Yang;Mark Zwolinski
Affiliations:
-;-
Venue:
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Year:
2000

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Abstract

This paper presents a method for determine an optimal set of components for a density mixture model using mutual information. A component with small mutual information is believed to be independent from the rest components and to make a significant contribution to the system and hence cannot be removed. Whilst a component with large mutual information is believed to be unlikely independent from the rest components within a system and hence can be removed. Continuing removing components with positive mutual information till the system mutual information becomes non-positive will finally give rise to a parsimonious structure for a density mixture model. The method has been verified with several examples.