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

被引：44

作者：

Yeung, DY ^{[1
]}

Chang, H ^{[1
]}

机构：

[1] Hong Kong Univ Sci & Technol, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China

来源：

PATTERN RECOGNITION | 2006年 / 39卷 / 05期

关键词：

metric learning; mahalanobis metric; semi-supervised learning;

D O I：

10.1016/j.patcog.2005.12.004

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1007 / 1010

页数：4