Overview on kernels for least-squares support-vector-macihine-based clustering: explaining kernel expectral clustering.

Overview on kernels for least-squares support-vector-macihine-based clustering: explaining kernel expectral clustering. Fernández, Y., Marrufo, I., Paez, M., A., Umaquinga-Criollo, A., C., Rosero, P., D., & Peluffo-Ordóñez, H., D. REVISTA INVESTIGACION OPERACIONAL, 2021.

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This letter presents an overview on some remarkable basics on kernels as well as the formulation of a clustering approach based on least-squares support vector machines. Speciﬁcally, the method known as kernel spectral clustering (KSC) is of interest. We explore the links between KSC and a weighted version of kernel principal component analysis (WKPCA). Also, we study the solution of the KSC problem by means of a primal-dual scheme. All mathematical developments are carried out following an entirely matrix formulation. As a result, in addition to the elegant KSC formulation, important insights and hints about the use and design of kernel-based approaches for clustering are provided.

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 title = {Overview on kernels for least-squares support-vector-macihine-based clustering: explaining kernel expectral clustering.},
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 year = {2021},
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 bibtype = {article},
 author = {Fernández, Y. and Marrufo, I. and Paez, M. A. and Umaquinga-Criollo, A. C. and Rosero, P. D. and Peluffo-Ordóñez, H. D.},
 journal = {REVISTA INVESTIGACION OPERACIONAL}
}

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