Feature selection for clustering on high dimensional data

Hong Zeng*, Yiu Ming CHEUNG

*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

This paper addresses the problem of feature selection for the high dimensional data clustering. This is a difficult problem because the ground truth class labels that can guide the selection are unavailable in clustering. Besides, the data may have a large number of features and the irrelevant ones can ruin the clustering. In this paper, we propose a novel feature weighting scheme for a kernel based clustering criterion, in which the weight for each feature is a measure of its contribution to the clustering task. Accordingly, we give a well-defined objective function, which can be explicitly solved in an iterative way. Experimental results show the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationPRICAI 2008
Subtitle of host publicationTrends in Artificial Intelligence - 10th Pacific Rim International Conference on Artificial Intelligence, Proceedings
Pages913-922
Number of pages10
DOIs
Publication statusPublished - 2008
Event10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008 - Hanoi, Viet Nam
Duration: 15 Dec 200819 Dec 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5351 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008
Country/TerritoryViet Nam
CityHanoi
Period15/12/0819/12/08

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'Feature selection for clustering on high dimensional data'. Together they form a unique fingerprint.

Cite this