Learning prototypes and distances: A prototype reduction technique based on nearest neighbor error minimization

Authors:
Roberto Paredes;Enrique Vidal
Affiliations:
Universidad Politecnica de Valencia, DSIC, Camino de Vera S/N, 46022 Valencia, Spain;Universidad Politecnica de Valencia, DSIC, Camino de Vera S/N, 46022 Valencia, Spain
Venue:
Pattern Recognition
Year:
2006

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Abstract

A prototype reduction algorithm is proposed, which simultaneously trains both a reduced set of prototypes and a suitable local metric for these prototypes. Starting with an initial selection of a small number of prototypes, it iteratively adjusts both the position (features) of these prototypes and the corresponding local-metric weights. The resulting prototypes/metric combination minimizes a suitable estimation of the classification error probability. Good performance of this algorithm is assessed through experiments with a number of benchmark data sets and with a real task consisting in the verification of images of human faces.