Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/2482

Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints

Authors Yeung, Dit-Yan
Chang, Hong
Issue Date 2006
Source Pattern recognition , v. 39, (5), 2006, MAY, p. 1007-1010
Summary Relevant component analysis (RCA) is a recently proposed metric learning method for semi-supervised learning applications. It is a simple and efficient method that has been applied successfully to give impressive results. However, RCA can make use of supervisory information in the form of positive equivalence constraints only. In this paper, we propose an extension to RCA that allows both positive and negative equivalence constraints to be incorporated. Experimental results show that the extended RCA algorithm is effective. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Subjects
ISSN 0031-3203
Rights Pattern Recognition © copyright (2006) Elsevier. The Journal's web site is located at http://www.sciencedirect.com
Language English
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