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

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. Key words: metric learning, Mahalanobis metric, semi-supervised learning.

• Type: Journal
• Year: 2006
• Where: PR
• Authors: Dit-Yan Yeung, Hong Chang
• Keywords: PR 2006, Relevant Component Analysis, Semi-supervised Learning, Semi-supervised Learning Applications