Publication Type

Journal Article

Publication Date

2012

Abstract

Learning distance functions with side information plays a key role in many data mining applications. Conventional distance metric learning approaches often assume that the target distance function is represented in some form of Mahalanobis distance. These approaches usually work well when data are in low dimensionality, but often become computationally expensive or even infeasible when handling high-dimensional data. In this paper, we propose a novel scheme of learning nonlinear distance functions with side information. It aims to learn a Bregman distance function using a nonparametric approach that is similar to Support Vector Machines. We emphasize that the proposed scheme is more general than the conventional approach for distance metric learning, and is able to handle high-dimensional data efficiently. We verify the efficacy of the proposed distance learning method with extensive experiments on semi-supervised clustering. The comparison with state-of-the-art approaches for learning distance functions with side information reveals clear advantages of the proposed technique.

Keywords

Bregman distance, convex functions, distance functions, metric learning

Discipline

Computer Sciences

Publication

IEEE Transactions on Knowledge and Data Engineering (TKDE)

Volume

24

Issue

3

First Page

478-491

ISSN

1041-4347

Identifier

10.1109/TKDE.2010.215

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