A study on semi-supervised dissimilarity representation

In the dissimilarity representation approach, objects are represented by their dissimilarities with respect to a representation set, rather than by features. Up to now, the representation or prototype set has usually been selected from the training data, limiting the different aspects that can be captured, especially when the training data set is small. This paper studies the performance change if the object´s representation is extended by including also test data into the representation set in a semi-supervised setting. Experiments on a set of standard data show that the semi-supervised setting can substantially improve the performance of the dissimilarity based representation especially for the small sample size problem.