TY - GEN
T1 - Feature selection for clustering on high dimensional data
AU - Zeng, Hong
AU - CHEUNG, Yiu Ming
N1 - Copyright:
Copyright 2009 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=58349111398&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-89197-0_85
DO - 10.1007/978-3-540-89197-0_85
M3 - Conference proceeding
AN - SCOPUS:58349111398
SN - 354089196X
SN - 9783540891963
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 913
EP - 922
BT - PRICAI 2008
T2 - 10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008
Y2 - 15 December 2008 through 19 December 2008
ER -