The dissimilarity representation for structural pattern recognition
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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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.