Robust self-tuning spectral clustering

Abstract

Clustering, as an effective data analysis technique, is widely used in industrial application and science research. In this paper, we have proposed a novel spectral clustering method to solve three problems of spectral clustering, i.e., cluster-initialization, cluster-specification, and noise-robustness. To do this, we first learn a high-quality affinity matrix, and then capture an inherent clustering pattern in real feature space and projected subspace, and finally extract connected components to conduct clustering. By comparing to five comparison methods in six real data sets, our proposed method has achieved a competitive clustering performance in terms of four evaluation metrics.