posted on 2010-12-01, 00:00authored byJ Wang, S Wu, Gang LiGang Li
Selecting a suitable proximity measure is one of the fundamental tasks in clustering. How to effectively utilize all available side information, including the instance level information in the form of pair-wise constraints, and the attribute level information in the form of attribute order preferences, is an essential problem in metric learning. In this paper, we propose a learning framework in which both the pair-wise constraints and the attribute order preferences can be incorporated simultaneously. The theory behind it and the related parameter adjusting technique have been described in details. Experimental results on benchmark data sets demonstrate the effectiveness of proposed method.
History
Journal
International journal of computational intelligence systems
Volume
3
Pagination
770 - 785
Location
Paris, France
Open access
Yes
ISSN
1875-6891
eISSN
1875-6883
Language
eng
Notes
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