Highly Sparse Kernel Spectral Clustering with Predictive Out-of-Sample Extensions
Carlos M. Alzate, Johan A. K. Suykens · 2010
Abstract. Kernel spectral clustering has been formulated as a primal- dual optimization setting allowing natural extensions to out-of-sample data together with model selection in a learning framework which is important for obtaining a good generalization performance. In this paper, we propose a new sparse method for kernel spectral clustering. The approach exploits the structure of the eigenvectors and the corresponding projections of the data when the clusters are well formed. Experimental results with toy data and images show highly sparse clustering models with predictive capabilities. 1