The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon

In this paper, we study the use of unlabeled samples in reducing the problem of small training sample size that can severely affect the recognition rate of classifiers when the dimensionality of the multispectral data is high. We show that by using additional unlabeled samples that are available at no extra cost, the performance may be improved, and therefore the Hughes phenomenon can be mitigated. Furthermore, by experiments, we show that by using additional unlabeled samples more representative estimates can be obtained. We also propose a semiparametric method for incorporating the training (i.e., labeled) and unlabeled samples simultaneously into the parameter estimation process.