An Empirical Comparison of Kernel-Based and Dissimilarity-Based Feature Spaces

The aim of this paper is to find an answer to the question: What is the difference between dissimilarity-based classifications(DBCs) and other kernelbased classifications(KBCs)? In DBCs [11], classifiers are defined among classes; they are not based on the feature measurements of individual objects, but rather on a suitable dissimilarity measure among them. In KBCs [15], on the other hand, classifiers are designed in a high-dimensional feature space transformed from the original input feature space through kernels, such as a Mercer kernel. Thus, the difference that exists between the two approaches can be summarized as follows: The distance kernel of DBCs represents the discriminative information in a relative manner, i.e. through pairwise dissimilarity relations between two objects, while the mapping kernel of KBCs represents the discriminative information uniformly in a fixed way for all objects. In this paper, we report on an empirical evaluation of some classifiers built in...

• Type: Journal
• Year: 2010
• Where: SSPR
• Authors: Sang-Woon Kim, Robert P. W. Duin
• Keywords: DBCs, Discriminative Information, KBCs, Pattern Recognition, SSPR 2010