Feature selection simultaneously preserving both class and cluster
  structures
Suchismita Das Nikhil R. Pal
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.