Abstract

This paper compares two alternative feature data meta-representations using Intervals’ Numbers (INs) in the context of the Minimum Distance Classifier (MDC) model. The first IN meta-representation employs one IN per feature vector, whereas the second IN meta-representation employs one IN per feature per class. Comparative classification experiments with the standard minimum distance classifier (MDC) on two benchmark classification problems, regarding face/facial expression recognition, demonstrate the superiority of the aforementioned second IN meta-representation. This superiority is attributed to an IN’s capacity to represent discriminative, all-order data statistics in a population of features.

Citation

G.A. Papakostas, V.G. Kaburlasos, “Lattice Computing (LC) meta-representation for pattern classification”, Proceedings of the World Congress on Computational Intelligence (WCCI) 2014, FUZZ-IEEE Program, Beijing, China, 6-11 July 2014, pp. 39-44.

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