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Relational discriminant analysis
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DOI：10.1016/S0167-8655(99)00085-9.png)
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Abstract
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Relational discriminant analysis is based on a proximity description of the data. Instead of features, the similarities to a subset of the objects in the training data are used for representation. In this paper we will show that this subset might be small and that its exact choice is of minor importance. Moreover, it is shown that linear or non-linear methods for feature extraction based on multi-dimensional scaling are not, or just hardly better than subsets. Selection drastically simplifies the problem of dimension reduction. Relational discriminant analysis may thus be a valuable pattern recognition tool for applications in which the choice of the features is uncertain. (C) 1999 Elsevier Science B.V. All rights reserved.
Keywords:
featureless pattern recognition
support vector classifiers
proximities
dimension reduction
non-linear mapping
multidimensional scaling
distance mapping
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