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. © 2008 Springer Berlin Heidelberg.
CITATION STYLE
Zeng, H., & Cheung, Y. M. (2008). Feature selection for clustering on high dimensional data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5351 LNAI, pp. 913–922). https://doi.org/10.1007/978-3-540-89197-0_85
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