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

Yiu Ming CHEUNG*, Hong Zeng

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings
PublisherSpringer Verlag
Pages78-87
Number of pages10
EditionPART 1
ISBN (Print)9783540746898
DOIs
Publication statusPublished - 2007
Event17th International Conference on Artificial Neural Networks, ICANN 2007 - Porto, Portugal
Duration: 9 Sep 200713 Sep 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume4668 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Artificial Neural Networks, ICANN 2007
Country/TerritoryPortugal
CityPorto
Period9/09/0713/09/07

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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