Repeated Observations for Classification

Abstract

We study the problem nonparametric classification with repeated observations. Let be the d dimensional feature vector and let Y denote the label taking values in \1, ,M\. In contrast to usual setup with large sample size n and relatively low dimension d, this paper deals with the situation, when instead of observing a single feature vector we are given t repeated feature vectors _1, ,_t . Some simple classification rules are presented such that the conditional error probabilities have exponential convergence rate of convergence as t. In the analysis, we investigate particular models like robust detection by nominal densities, prototype classification, linear transformation, linear classification, scaling.

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Reproductions