An adaptive classifier design for high-dimensional data analysis with a limited training data set

TLDR

The study proposes a self‑learning adaptive classifier to overcome the Hughes phenomenon caused by limited training samples in high‑dimensional multispectral data. The classifier iteratively incorporates semilabeled samples, assigning full weight to original training data and reduced weight to semilabeled data to control their influence. Experiments demonstrate that incorporating semilabeled samples improves statistical estimation and classification accuracy, effectively mitigating the Hughes phenomenon.

Abstract

Proposes a self-learning and self-improving adaptive classifier to mitigate the problem of small training sample size that can severely affect the recognition accuracy of classifiers when the dimensionality of the multispectral data is high. This proposed adaptive classifier utilizes classified samples (referred to as semilabeled samples) in addition to original training samples iteratively. In order to control the influence of semilabeled samples, the proposed method gives full weight to the training samples and reduced weight to semilabeled samples. The authors show that by using additional semilabeled samples that are available without extra cost, the additional class label information may be extracted and utilized to enhance statistics estimation and hence improve the classifier performance, and therefore the Hughes phenomenon (peak phenomenon) may be mitigated. Experimental results show this proposed adaptive classifier can improve the classification accuracy as well as representation of estimated statistics significantly.