A Study on Combining Sets of Differently Measured Dissimilarities

The ways distances are computed or measured enable us to have different representations of the same objects. In this paper we want to discuss possible ways of merging different sources of information given by differently measured dissimilarity representations. We compare here a simple averaging scheme [1] with dissimilarity forward selection and other techniques based on the learning of weights of linear and quadratic forms. Our general conclusion is that, although the more advanced forms of combination cannot always lead to better classification accuracies, combining given distance matrices prior to training is always worthwhile. We can thereby suggest which combination schemes are preferable with respect to the problem data.