  In this paper, we investigate the problem of classifying feature vectors with
mutually independent but non-identically distributed elements. First, we show
the importance of this problem. Next, we propose a classifier and derive an
analytical upper bound on its error probability. We show that the error
probability goes to zero as the length of the feature vectors grows, even when
there is only one training feature vector per label available. Thereby, we show
that for this important problem at least one asymptotically optimal classifier
exists. Finally, we provide numerical examples where we show that the
performance of the proposed classifier outperforms conventional classification
algorithms when the number of training data is small and the length of the
feature vectors is sufficiently high.
