Feature selection simultaneously preserving both class and cluster structures
This addresses a gap in feature selection methods for data with divergent class and cluster structures, offering a novel integrated approach.
The paper tackles the problem of feature selection when data has distinct class and cluster structures, proposing a neural network-based method that simultaneously considers class discrimination and cluster preservation. The results show it can select features effective for both classification and clustering, with applications including hyperspectral image band selection.
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.