Lattice computing (LC) meta-representation for pattern classification

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.