Hits:

Indexed by:期刊论文

Date of Publication:2011-10-01

Journal:NEUROCOMPUTING

Included Journals:Scopus、SCIE、EI

Volume:74

Issue:17

Page Number:3598-3608

ISSN No.:0925-2312

Key Words:Semi-supervised learning; Subspace clustering; Constraint inconsistence

Abstract:Selecting correct dimensions is very important to subspace clustering and is a challenging issue. This paper studies semi-supervised approach to the problem. In this setting, limited domain knowledge in the form of space level pair-wise constraints, i.e., must-links and cannot-links, are available. We propose a semi-supervised subspace clustering (S(3)C) algorithm that exploits constraint inconsistence for dimension selection. Our algorithm firstly correlates globally inconsistent constraints to dimensions in which they are consistent, then unites constraints with common correlating dimensions, and finally forms the subspaces according to the constraint unions. Experimental results show that S(3)C is superior to the typical unsupervised subspace clustering algorithm FINDIT, and the other constraint based semi-supervised subspace clustering algorithm SC-MINER. (C) 2011 Elsevier B.V. All rights reserved.