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

Pattern Recognition

Volumn 39, Issue 5, 2006, Pages 1007-1010

  Yeung, Dit Yan ^a   Chang, Hong ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Mahalanobis metric; Metric learning; Semi supervised learning

Indexed keywords

CONSTRAINT THEORY; INFORMATION ANALYSIS; LEARNING SYSTEMS; PRINCIPAL COMPONENT ANALYSIS;

MAHALANOBIS METRIC; METRIC LEARNING; SEMI-SUPERVIZED LEARNING;

ALGORITHMS;

EID: 33244489358     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2005.12.004     Document Type: Article

References (4)

1. A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall Learning distance functions using equivalence relations Proceedings of the Twentieth International Conference on Machine Learning Washington, DC, USA 21-24 August 2003 11 18
2. A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall Learning a Mahalanobis metric from equivalence constraints J. Mach. Learning Res. 6 2005 937 965
3. H. Chang, and D.Y. Yeung Locally linear metric adaptation for semi-supervised clustering Proceedings of the Twenty-First International Conference on Machine Learning Banff, Alberta, Canada 4-8 July 2004 153 160
4. E.P. Xing, A.Y. Ng, M.I. Jordan, and S. Russell Distance metric learning, with application to clustering with side-information S. Becker S. Thrun K. Obermayer Advances in Neural Information Processing Systems vol. 15 2003 MIT Press Cambridge, MA, USA 505 512