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Please use this identifier to cite or link to this item:
http://hdl.handle.net/1783.1/2482
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| 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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| yeung.pr2006a.pdf | pre-published version | 155Kb | Adobe PDF | View/Open |
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