Repeated Observations for Classification
Hüseyin Afşer László Györfi Harro Walk
We study the problem nonparametric classification with repeated observations.
Let $\bX$ be the $d$ dimensional feature vector and let $Y$ denote the label
taking values in $\{1,\dots ,M\}$. 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 $\bX$ we are given $t$
repeated feature vectors $\bV_1,\dots ,\bV_t $. Some simple classification
rules are presented such that the conditional error probabilities have
exponential convergence rate of convergence as $t\to\infty$. In the analysis,
we investigate particular models like robust detection by nominal densities,
prototype classification, linear transformation, linear classification,
scaling.