Constrained Empirical Risk Minimization Framework For Distance Metric Learning

Publisher:
IEEE-inst Electrical Electronics Engineers Inc
Publication Type:
Journal Article
Citation:
IEEE Transactions On Neural Networks And Learning Systems, 2012, 23 (8), pp. 1194 - 1205
Issue Date:
2012-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2012001394OK.pdf772.55 kB
Adobe PDF
Distance metric learning (DML) has received increasing attention in recent years. In this paper, we propose a constrained empirical risk minimization framework for DML. This framework enriches the state-of-the-art studies on both theoretic and algorithmi
Please use this identifier to cite or link to this item: