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On the relationship between dimensionality reduction and spectral clustering from a kernel viewpoint

Peluffo-Ordóñez, Diego H. Y Anaya, Andres J. Y Alvarado, Juan C. Y Becerra, Miguel A. Y Castro, Andres E. Y Blanco, Xiomara. (2016) On the relationship between dimensionality reduction and spectral clustering from a kernel viewpoint. Advances In Intelligent Systems And Computing, 474. pp. 255-264. ISSN 2194-5357

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This paper presents the development of a unified view of spectral clustering and unsupervised dimensionality reduction approaches within a generalized kernel framework. To do so, the authors propose a multipurpose latent variable model in terms of a high-dimensional representation of the input data matrix, which is incorporated into a least-squares support vector machine to yield a generalized optimization problem. After solving it via a primal-dual procedure, the final model results in a versatile projected matrix able to represent data in a low-dimensional space, as well as to provide information about clusters. Specifically, our formulation yields solutions for kernel spectral clustering and weighted-kernel principal component analysis.

Tipo de Elemento: Artículo
Palabras Clave: Dimensionality reduction Generalized kernel formulation Kernel PCA Spectral clustering Support vector machine
Asunto: Q Ciencias > QA Mathematics > QA75 Electronic computers. Computer science
T Tecnología > TK Electrical engineering. Electronics Nuclear engineering
Division: Facultad de Ingeniería > Programa de Ingeniería Electrónica > Productividad
Depósito de Usuario: Andres Pantoja
Fecha Deposito: 28 Jun 2018 04:45
Ultima Modificación: 28 Jun 2018 04:45

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