A Generalized Kernel Approach to Dissimilarity-based Classification
Elzbieta Pekalska, Pavel Paclik, Robert P.W. Duin;
2(Dec):175-211, 2001.
Abstract
Usually, objects to be classified are represented by features.
In this paper, we discuss an alternative object representation based on
dissimilarity values. If such distances separate the classes well,
the nearest neighbor method offers a good solution. However, dissimilarities
used in practice are usually far from ideal and the performance of the
nearest neighbor rule suffers from its sensitivity to noisy examples.
We show that other, more global classification techniques are preferable
to the nearest neighbor rule, in such cases.
For classification purposes, two different ways of using generalized
dissimilarity kernels are considered. In the first one, distances
are isometrically embedded in a pseudo-Euclidean space and the classification
task is performed there. In the second approach, classifiers are built
directly on distance kernels. Both approaches are described theoretically
and then compared using experiments with different dissimilarity measures
and datasets including degraded data simulating the problem of missing values.
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