Generalizing dissimilarity representations using feature lines

Authors:
Mauricio Orozco-Alzate;Robert P. W. Duin;César Germán Castellanos-Domínguez
Affiliations:
Inf. and Comm. Theory Group, Fac. of Electrical Eng., Mathematics and Comp. Sci., Delft Univ. of Techn., Delft, The Netherlands and Grupo de Control y Procesamiento Digital de Señales, Univ. ...;Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands;Grupo de Control y Procesamiento Digital de Señales, Universidad Nacional de Colombia Sede Manizales, Manizales, Caldas, Colombia
Venue:
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Year:
2007

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Abstract

A crucial issue in dissimilarity-based classification is the choice of the representation set. In the small sample case, classifiers capable of a good generalization and the injection or addition of extra information allow to overcome the representational limitations. In this paper, we present a new approach for enriching dissimilarity representations. It is based on the concept of feature lines and consists in deriving a generalized version of the original dissimilarity representation by using feature lines as prototypes. We use a linear normal density-based classifier and the nearest neighbor rule, as well as two different methods for selecting prototypes: random choice and a length-based selection of the feature lines. An important observation is that just a few long feature lines are needed to obtain a significant improvement in performance over the other representation sets and classifiers. In general, the experiments show that this alternative representation is especially profitable for some correlated datasets.