Localized Feature Selection for Clustering and its Application in Image Grouping

In clustering, global feature selection algorithms attempt to select a common feature subset that is relevant for all clusters. Consequently, they are not able to identify individual clusters that exist in different feature subspaces. In this paper, we propose a localized feature selection algorithm for clustering. The proposed algorithm computes adjusted and normalized scatter separability for individual clusters. A sequential backward search is then applied to find the optimal (maybe local) feature subsets for each cluster. Experiment results on both synthetic data clustering and content-based image grouping show the need for feature selection in clustering and the benefits of selecting features locally.