Abstract
The selection of prototypes for the dissimilarity space is a key aspect to overcome problems related to the curse of dimensionality and computational burden. How to properly define and select the prototypes is still an open issue. In this paper, we propose the selection of clusters as prototypes to create low-dimensional spaces. Experimental results show that the proposed approach is useful in the problems presented. Especially, the use of the minimum distances to clusters for representation provides good results.
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Pekalska, 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)
Lozano, M., Sotoca, J.M., Sánchez, J.S., Pla, F., Pekalska, E., Duin, R.P.W.: Experimental study on prototype optimisation algorithms for prototype-based classification in vector spaces. Pattern Recogn. 39(10), 1827–1838 (2006)
Pekalska, E., Duin, R.P.W., Paclík, P.: Prototype selection for dissimilarity-based classifiers. Pattern Recogn. 39(2), 189–208 (2006)
Plasencia-Calaña, Y., Orozco-Alzate, M., García-Reyes, E., Duin, R.P.W.: Selecting feature lines in generalized dissimilarity representations for pattern recognition. Digit. Signal Process. 23(3), 902–911 (2013)
Riesen, K., Bunke, H.: Graph classification by means of Lipschitz embedding. Trans. Sys. Man Cyber. Part B 39(6), 1472–1483 (2009)
Plasencia-Calaña, Y., García-Reyes, E., Orozco-Alzate, M., Duin, R.P.W.: Prototype selection for dissimilarity representation by a genetic algorithm. In: Proceedings of the 20th International Conference on Pattern Recognition, ICPR 2010, pp. 177–180. IEEE Computer Society, Washington, DC (2010)
Frey, B.J.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)
Cox, T.F., Cox, M.: Multidimensional Scaling, 2nd edn. Chapman and Hall/CRC (2000)
Bengio, Y., Paiement, J.F., Vincent, P., Delalleau, O., Roux, N.L., Ouimet, M.: Out-of-sample extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering. In: Advances in Neural Information Processing Systems, pp. 177–184. MIT Press (2003)
Baker, C.T.H.: The numerical treatment of integral equations. Clarendon Press, Oxford (1977)
Sigillito, V.G., Wing, S.P., Hutton, L.V., Baker, K.B.: Classification of radar returns from the ionosphere using neural networks. Johns Hopkins APL Technical Digest, 262–266 (1989)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)
Sebastian, T.B., Klein, P.N., Kimia, B.B.: Recognition of shapes by editing their shock graphs. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 550–571 (2004)
Bunke, H., Buhler, U.: Applications of approximate string matching to 2D shape recognition. Pattern Recogn. 26(12), 1797–1812 (1993)
Breiman, L.: Bias, Variance, and Arcing Classifiers. Technical report, University of California, Berkeley (1996)
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Plasencia-Calaña, Y., Orozco-Alzate, M., García-Reyes, E., Duin, R.P.W. (2013). Towards Cluster-Based Prototype Sets for Classification in the Dissimilarity Space. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41822-8_37
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DOI: https://doi.org/10.1007/978-3-642-41822-8_37
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