A maximum weighted likelihood approach to simultaneous model selection and feature weighting in Gaussian mixture

Authors:
Yiu-ming Cheung;Hong Zeng
Affiliations:
Department of Computer Science, Hong Kong Baptist University, Hong Kong;Department of Computer Science, Hong Kong Baptist University, Hong Kong
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

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Abstract

This paper is to identify the clustering structure and the relevant features automatically and simultaneously in the context of Gaussian mixture model. We perform this task by introducing two sets of weight functions under the recently proposed Maximum Weighted Likelihood (MWL) learning framework. One set is to reward the significance of each component in the mixture, and the other one is to discriminate the relevance of each feature to the cluster structure. The experiments on both the synthetic and real-world data show the efficacy of the proposed algorithm.