Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Relaxational metric adaptation and its application to semi-supervised clustering and content-based image retrieval

Language

English

Abstract

The performance of many supervised and unsupervised learning algorithms is very sensitive to the choice of an appropriate distance metric. Previous work in metric learning and adaptation has mostly been focused on classification tasks by making use of class label information. In standard clustering tasks, however, class label information is not available. In order to adapt the metric to improve the clustering results, some background knowledge or side information is needed. One useful type of side information is in the form of pairwise similarity or dissimilarity information. Recently, some novel methods (e.g., the parametric method proposed by Xing et al.) for learning global metrics based on pairwise side information have been shown to demonstrate promising results. In this paper, we propose a nonparametric method, called relaxational metric adaptation (RMA), for the same metric adaptation problem. While RMA is local in the sense that it allows locally adaptive metrics, it is also global because even patterns not in the vicinity can have long-range effects on the metric adaptation process. Experimental results for semi-supervised clustering based on both simulated and real-world data sets show that RMA outperforms Xing et al.'s method under most situations. Besides applying RMA to semi-supervised learning, we have also used it to improve the performance of content-based image retrieval systems through metric adaptation. Experimental results based on two real-world image databases show that RMA significantly outperforms other methods in improving the image retrieval performance.

Keywords

Distance metric, Nonparametric method, Semi-supervised clustering, Constrained k-means, Side information, Pairwise similarity and dissimilarity, Content-based image retrieval

Publication Date

10-2006

Source Publication Title

Pattern Recognition

Volume

39

Issue

10

Start Page

1905

End Page

1917

Publisher

Elsevier

Peer Reviewed

1

Funder

The research described in this paper has been supported by two grants, CA03/04.EG01 (which is part of HKBU2/03/C) and HKUST6174/04E, from the Research Grants Council of the Hong Kong Special Administrative Region, China.

DOI

10.1016/j.patcog.2006.04.006

Link to Publisher's Edition

http://dx.doi.org/10.1016/j.patcog.2006.04.006

ISSN (print)

00313203

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