Abstract
A widely used approach to cope with asymmetry in dissimilarities is by symmetrizing them. Usually, asymmetry is corrected by applying combiners such as average, minimum or maximum of the two directed dissimilarities. Whether or not these are the best approaches for combining the asymmetry remains an open issue. In this paper we study the performance of the extended asymmetric dissimilarity space (EADS) as an alternative to represent asymmetric dissimilarities for classification purposes. We show that EADS outperforms the representations found from the two directed dissimilarities as well as those created by the combiners under consideration in several cases. This holds specially for small numbers of prototypes; however, for large numbers of prototypes the EADS may suffer more from overfitting than the other approaches. Prototype selection is recommended to overcome overfitting in these cases.
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References
Bowdle, B., Gentner, D.: Informativity and asymmetry in comparisons. Cogn. Psychol. 34(3), 244–286 (1997)
Briggs, F., Lakshminarayanan, B., Neal, L., Fern, X., Raich, R., Hadley, S., Hadley, A., Betts, M.: Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach. J. Acoust. Soc. Am. 131, 4640 (2012)
Bunke, H., Bühler, U.: Applications of approximate string matching to 2D shape recognition. Pattern Recogn. 26(12), 1797–1812 (1993)
Bunke, H., Riesen, K.: Graph classification based on dissimilarity space embedding. In: da Vitoria Lobo, N., Kasparis, T., Roli, F., Kwok, J.T., Georgiopoulos, M., Anagnostopoulos, G.C., Loog, M. (eds.) S+SSPR 2008. LNCS, vol. 5342, pp. 996–1007. Springer, Heidelberg (2008)
Cheplygina, V., Tax, D.M.J., Loog, M.: Class-dependent dissimilarity measures for multiple instance learning. In: Gimel’farb, G., Hancock, E., Imiya, A., Kuijper, A., Kudo, M., Omachi, S., Windeatt, T., Yamada, K. (eds.) SSPR&SPR 2012. LNCS, vol. 7626, pp. 602–610. Springer, Heidelberg (2012)
Dietterich, T., Lathrop, R., Lozano-Pérez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89(1-2), 31–71 (1997)
Dinh, C., Duin, R.P.W., Loog, M.: A study on semi-supervised dissimilarity representation. In: International Conference on Pattern Recognition (2012)
Duin, R.P.W., Juszczak, P., Paclik, P., Pękalska, E., De Ridder, D., Tax, D.M.J., Verzakov, S.: A Matlab toolbox for pattern recognition. PRTools version 3 (2000)
Duin, R.P.W., Pekalska, E.: Non-Euclidean dissimilarities: causes and informativeness. In: Proceedings of the 2010 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition. pp. 324–333. SSPR & SPR’10, Springer-Verlag, Berlin, Heidelberg (2010)
Duin, R.P.W., Pękalska, E.: The dissimilarity space: bridging structural and statistical pattern recognition. Pattern Recogn. Lett. 33(7), 826–832 (2012)
Gärtner, T., Flach, P., Kowalczyk, A., Smola, A.: Multi-instance kernels. In: Proc. of the 19th Int. Conf. on Machine Learning, pp. 179–186 (2002)
Jain, A.K., Zongker, D.: Representation and recognition of handwritten digits using deformable templates. IEEE Trans. Pattern Anal. Mach. Intell. 19, 1386–1391 (1997)
Muñoz, A., de Diego, I.M., Moguerza, J.M.: Support vector machine classifiers for asymmetric proximities. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN/ICONIP 2003. LNCS, vol. 2714, pp. 217–224. Springer, Heidelberg (2003)
Pękalska, E., Duin, R.P.W.: The Dissimilarity Representation for Pattern Recognition: Foundations and Applications (Machine Perception and Artificial Intelligence). World Scientific Publishing Co. Inc., River Edge (2005)
Pękalska, E., Duin, R.P.W.: Beyond traditional kernels: Classification in two dissimilarity-based representation spaces. IEEE Trans. Syst. Man Cybern. C, Appl. Rev. 38(6), 729–744 (2008)
Pękalska, E., Duin, R.P.W., Paclík, P.: Prototype selection for dissimilarity-based classifiers. Pattern Recogn. 39(2), 189–208 (2006)
Pękalska, E., Paclik, P., Duin, R.P.W.: A generalized kernel approach to dissimilarity-based classification. J. Mach. Learn. Res. 2, 175–211 (2002)
Plasencia-Calaña, Y., García-Reyes, E.B., Duin, R.P.W., Orozco-Alzate, M.: On using asymmetry information for classification in extended dissimilarity spaces. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds.) CIARP 2012. LNCS, vol. 7441, pp. 503–510. Springer, Heidelberg (2012)
Rahmani, R., Goldman, S., Zhang, H., Krettek, J., Fritts, J.: Localized content based image retrieval. In: Proc. of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 227–236. ACM (2005)
Riesen, K., Neuhaus, M., Bunke, H.: Graph embedding in vector spaces by means of prototype selection. In: Escolano, F., Vento, M. (eds.) GbRPR. LNCS, vol. 4538, pp. 383–393. Springer, Heidelberg (2007)
Schölkopf, B., Mika, S., Burges, C.J.C., Knirsch, P., Müller, K.R., Rätsch, G., Smola, A.J.: Input space versus feature space in kernel-based methods. IEEE Trans. Neural Netw. 10(5), 1000–1017 (1999)
Tax, D.M.J., Loog, M., Duin, R.P.W., Cheplygina, V., Lee, W.-J.: Bag dissimilarities for multiple instance learning. In: Pelillo, M., Hancock, E.R. (eds.) SIMBAD 2011. LNCS, vol. 7005, pp. 222–234. Springer, Heidelberg (2011)
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Plasencia-Calaña, Y. et al. (2013). On the Informativeness of Asymmetric Dissimilarities. In: Hancock, E., Pelillo, M. (eds) Similarity-Based Pattern Recognition. SIMBAD 2013. Lecture Notes in Computer Science, vol 7953. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39140-8_5
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DOI: https://doi.org/10.1007/978-3-642-39140-8_5
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