Dissimilarity representations allow for building good classifiers

Pekalska, E

doi:10.1016/S0167-8655(02)00024-7

2002-06-01

167

PRE

Pekalska, E

Duin, RPW

DOI：10.1016/S0167-8655(02)00024-7

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摘要

En 中文

In this paper, a classification task on dissimilarity representations is considered. A traditional way to discriminate between objects represented by dissimilarities is the nearest neighbor method. It suffers, however, from a number of limitations, i.e., high computational complexity, a potential loss of accuracy when a small set of prototypes is used and sensitivity to noise. To overcome these shortcomings, we propose to use a normal density-based classifier constructed on the same representation. We show that such a classifier, based on a weighted combination of dissimilarities, can significantly improve the nearest neighbor rule with respect to the recognition accuracy and computational effort. (C) 2002 Elsevier Science B.V. All rights reserved.

Keyword:

similarity representations

normal density-based classifiers