Feature Weighted Rival Penalized EM for Gaussian Mixture Clustering: Automatic Feature and Model Selections in a Single Paradigm

Yiu Ming Cheung*, Hong Zeng

*Corresponding author for this work

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

Abstract

Recently, the Rival Penalized Expectation-Maximization (RPEM) algorithm (Cheung 2004 & 2005) has demonstrated its outstanding capability to perform the model selection automatically in the context of density mixture models. Nevertheless, the RPEM is unable to exclude the irrelevant variables (also called features) from the clustering process, which may degrade the algorithm's performance. In this paper, we adopt the concept of feature salience (Law et al. 2004) as the feature weight to measure the relevance of features to the cluster structure in the subspace, and integrate it into the RPEM algorithm. The proposed algorithm identifies the irrelevant features and estimates the number of clusters automatically and simultaneously in a single learning paradigm. Experiments show the efficacy of the proposed algorithm on both synthetic and benchmark real data sets.

Original languageEnglish
Title of host publicationComputational Intelligence and Security
Subtitle of host publicationInternational Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers
EditorsYuping Wang, Yiu-ming Cheung, Hailin Liu
Place of PublicationBerlin, Heidelberg
PublisherSpringer
Pages1018-1028
Number of pages11
Edition1st
ISBN (Electronic)9783540743774
ISBN (Print)9783540743767
DOIs
Publication statusPublished - 21 Aug 2007
Event2006 International Conference on Computational Intelligence and Security, CIS 2006 - Guangzhou, China
Duration: 3 Oct 20066 Oct 2006
https://ieeexplore.ieee.org/xpl/conhome/4072023/proceeding
https://link.springer.com/book/10.1007/978-3-540-74377-4

Publication series

NameLecture Notes in Computer Science
Volume4456
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence
NameCIS: International Conference on Computational and Information Science

Conference

Conference2006 International Conference on Computational Intelligence and Security, CIS 2006
Country/TerritoryChina
CityGuangzhou
Period3/10/066/10/06
Internet address

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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