Highly sparse kernel spectral clustering with predictive out-of-sample extensions

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… (More)

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