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. © Springer-Verlag 2013.
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CITATION STYLE
Plasencia-Calanã, Y., Orozco-Alzate, M., Garciá-Reyes, E., & Duin, R. P. W. (2013). Towards cluster-based prototype sets for classification in the dissimilarity space. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8258 LNCS, pp. 294–301). https://doi.org/10.1007/978-3-642-41822-8_37
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