Please use this identifier to cite or link to this item:

A kernel approach for semisupervised metric learning

Authors Yeung, Dit-Yan View this author's profile
Chang, Hong HKUST affiliated (currently or previously)
Issue Date 2007
Source IEEE transactions on neural networks , v. 18, (1), 2007, January, p. 141-149
Summary While distance function learning for supervised learning tasks has a long history, extending it to learning tasks with weaker supervisory information has only been studied recently. In particular, some methods have been proposed for semisupervised metric learning based on pairwise similarity or dissimilarity information. In this paper, we propose a kernel approach for semisupervised metric learning and present in detail two special cases of this kernel approach. The metric learning problem is thus formulated as an optimization problem for kernel learning. An attractive property of the optimization problem is that it is convex and, hence, has no local optima. While a closed-form solution exists for the first special case, the second case is solved using an iterative majorization procedure to estimate the optimal solution asymptotically. Experimental results based on both synthetic and real-world data show that this new kernel approach is promising for nonlinear metric learning.
ISSN 1045-9227
Rights © 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Language English
Format Article
Access View full-text via DOI
View full-text via Web of Science
View full-text via Scopus
Files in this item:
File Description Size Format
kernel.pdf 716580 B Adobe PDF