Fuzzy Double C-Means Clustering Based on Sparse Self-Representation

Abstract

This paper introduces the popular sparse representation method into the classical fuzzy c-means clustering algorithm, and presents a novel fuzzy clustering algorithm, called fuzzy double c-means based on sparse self-representation (FDCM_SSR). The major characteristic of FDCM_SSR is that it can simultaneously address two datasets with different dimensions, and has two kinds of corresponding cluster centers. The first one is the basic feature set that represents the basic physical property of each sample itself. The second one is learned from the basic feature set by solving a spare self-representation model, referred to as discriminant feature set, which reflects the global structure of the sample set. The spare self-representation model employs dataset itself as dictionary of sparse representation. It has good category distinguishing ability, noise robustness, and data-adaptiveness, which enhance the clustering and generalization performance of FDCM_SSR. Experiments on different datasets and images show that FDCM_SSR is more competitive than other state-of-the-art fuzzy clustering algorithms.