Feature selection simultaneously preserving both class and cluster structures

TL;DR

This paper introduces a neural network-based feature selection method that simultaneously preserves class discrimination and cluster structures, improving both classification and clustering performance, especially in hyperspectral image analysis.

Contribution

It proposes a novel neural network approach for feature selection that jointly considers class and cluster structure preservation, filling a gap in existing methods.

Findings

Effective in selecting features suitable for classification and clustering

Improves band selection in hyperspectral images

Demonstrates superior performance over traditional methods

Abstract

When a data set has significant differences in its class and cluster structure, selecting features aiming only at the discrimination of classes would lead to poor clustering performance, and similarly, feature selection aiming only at preserving cluster structures would lead to poor classification performance. To the best of our knowledge, a feature selection method that simultaneously considers class discrimination and cluster structure preservation is not available in the literature. In this paper, we have tried to bridge this gap by proposing a neural network-based feature selection method that focuses both on class discrimination and structure preservation in an integrated manner. In addition to assessing typical classification problems, we have investigated its effectiveness on band selection in hyperspectral images. Based on the results of the experiments, we may claim that the proposed feature/band selection can select a subset of features that is good for both classification and clustering.