Abstract
Dissimilarity representation is a very interesting alternative for the traditional feature space representation when addressing large multi-class problems or even problems with a small number of training samples. This paper describes the existing possibilities in terms of dissimilarity representation through some comprehensive examples. The justification for using such a problem representation strategy is discussed, followed by a complete review of the state-of-art and a critical analysis in which the original purpose of the dissimilarity representation and its perspectives are discussed. Dissimilarity space derived from automatically learned features and the possibility of transiting from one space to another when performing the tasks of the classification process are good examples of promising research directions in this field.
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