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Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/2482
Title: Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints
Authors: Yeung, Dit-Yan
Chang, Hong
Keywords: Metric learning
Mahalanobis metric
Semi-supervised learning
Issue Date: 13-Oct-2005
Citation: Pattern recognition, v. 39, iss. 5, May 2006, p. 1007-1010
Abstract: 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.
Rights: Pattern Recognition © copyright (2006) Elsevier. The Journal's web site is located at http://www.sciencedirect.com
URI: http://hdl.handle.net/1783.1/2482
Appears in Collections:CSE Journal/Magazine Articles

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