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

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. © Springer-Verlag Berlin Heidelberg 2007.