Iterative feature selection in Gaussian mixture clustering with automatic model selection

Hong Zeng*, Yiu Ming CHEUNG

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

This paper proposes an algorithm to deal with the feature selection in Gaussian mixture clustering by an iterative way: the algorithm iterates between the clustering and the unsupervised feature selection. First, we propose a quantitative measurement of the feature relevance with respect to the clustering. Then, we design the corresponding feature selection scheme and integrate it into the Rival Penalized EM (RPEM) clustering algorithm (Cheung 2005) that is able to determine the number of clusters automatically. Subsequently, the clustering can be performed in an appropriate feature subset by gradually eliminating the irrelevant features with automatic model selection. Compared to the existing methods, the numerical experiments have shown the efficacy of the proposed algorithm on the synthetic and real world data.

Original languageEnglish
Title of host publicationThe 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Pages2277-2282
Number of pages6
DOIs
Publication statusPublished - 2007
Event2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States
Duration: 12 Aug 200717 Aug 2007

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Conference

Conference2007 International Joint Conference on Neural Networks, IJCNN 2007
Country/TerritoryUnited States
CityOrlando, FL
Period12/08/0717/08/07

Scopus Subject Areas

  • Software

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