Abstract
The aim of this paper is to find an answer to the question: What is the difference between dissimilarity-based classifications(DBCs) and other kernel-based 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 the two different representation spaces: the dissimilarity space and the kernel space. Our experimental results, obtained with well-known benchmark databases, demonstrate that when the kernel parameters have not been appropriately chosen, DBCs always achieve better results than KBCs in terms of classification accuracies.
The work of the first author was partially done while visiting at Delft University of Technology, The Netherlands. We acknowledge financial support from the FET programme within the EU FP7, under the SIMBAD project (contract 213250). This work was supported by the National Research Foundation of Korea funded by the Korean Government (NRF-2009-0071283).
Chapter PDF
Similar content being viewed by others
Keywords
References
Balachander, T., Kothari, R.: Kernel based subspace pattern recognition. In: Proc. of Int’l Joint Conference on Neural Networks, Washington DC, USA, vol. 5, pp. 3119–3122 (1999)
Balcan, M.-F., Blum, A., Vempala, S.: Kernels as features: On kernels, margins, and low-dimensional mappings. Machine Learning 65, 79–94 (2006)
Baudat, G., Anouar, F.: Generalized discriminant analysis using a kernel approach. Neural Comput. 12(10), 2385–2404 (2000)
Chen, B., Yuan, L., Liu, H., Bao, Z.: Kernel subclass discriminant analysis. Neurocomputing 71, 455–458 (2007)
Goldfarb, L.: A unified approach to pattern recognition. Pattern Recogni. 17, 575–582 (1984)
Haasdonk, B.: Feature space interpretation of SVMs with indefinite kernels. IEEE Trans. Pattern Anal. and Machine Intell. 25(5), 482–492 (2005)
Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis: An overview with application to learning methods. Neural Comput. 16, 2639–2664 (2004)
Kim, S.-W., Oommen, B.J.: On using prototype reduction schemes to optimize dissimilarity-based classification. Pattern Recognition 40, 2946–2957 (2007)
Neuhaus, M., Bunke, H.: Edit distance-based kernel functions for structural pattern classification. Pattern Recognition 39, 1852–1863 (2006)
Paclik, P., Novovicova, J., Somol, P., Pudil, P.: Road sign classification using Laplace kernel classifier. Pattern Recognition Lett. 21(13-14), 1165–1173 (2000)
Pekalska, E., Duin, R.P.W.: The Dissimilarity Representation for Pattern Recognition: Foundations and Applications. World Scientific Publishing, Singapore (2005)
Pekalska, E., Duin, R.P.W.: Beyond traditional kernels: Classification in two dissimilarity-based representation spaces. IEEE Trans. Sys. Man, and Cybern(C) 38(6), 727–744 (2008)
Schölkopf, B., Smola, A.J., Müller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10, 1299–1319 (1998)
Sebastian, T.B., Klein, P.N., Kimia, B.B.: Recognition of shapes by editing shock graphs. In: Proc. of 8th IEEE Int’l Conf. on Computer Vision, Vancouver, Canada, pp. 755–762 (2001)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Tsagaroulis, T., Hamza, A.B.: Kernel locally linear embedding algorithm for quality control. In: Sobh, T., Elleithy, K., Mahmood, A., Karim, M.A. (eds.) Novel Algorithms and Techniques in Telecommunications, Automation and Industrial Electronics, pp. 1–6. Springer, Heidelberg (2008)
Wang, J., Lee, J., Zhang, C.: Kernel Trick Embedded Gaussian Mixture Model. In: Gavaldá, R., Jantke, K.P., Takimoto, E. (eds.) ALT 2003. LNCS (LNAI), vol. 2842, pp. 159–174. Springer, Heidelberg (2003)
Wilson, C.L., Garris, M.D.: Handprinted Character Database 3, Technical report, National Institute of Standards and Technology, Gaithersburg, Maryland (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, SW., Duin, R.P.W. (2010). An Empirical Comparison of Kernel-Based and Dissimilarity-Based Feature Spaces. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2010. Lecture Notes in Computer Science, vol 6218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14980-1_55
Download citation
DOI: https://doi.org/10.1007/978-3-642-14980-1_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14979-5
Online ISBN: 978-3-642-14980-1
eBook Packages: Computer ScienceComputer Science (R0)
