Dissimilarity-Based Classifications in Eigenspaces

This paper presents an empirical evaluation on a dissimilarity measure strategy by which dissimilarity-based classifications (DBCs) [10] can be efficiently implemented. In DBCs, classifiers are not based on the feature measurements of individual objects, but rather on a suitable dissimilarity measure among the objects. In image classification tasks, however, one of the most intractable problems to measure the dissimilarity is the distortion and lack of information caused by the differences in illumination and directions and outlier data. To overcome this problem, in this paper, we study a new way of performing DBCs in eigenspaces spanned, one for each class, by the subset of principal eigenvectors, extracted from the training data set through a principal component analysis. Our experimental results, obtained with well-known benchmark databases, demonstrate that when the dimensionality of the eigenspaces has been appropriately chosen, the DBCs can be improved in terms of classification accuracies.