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oai:gredos.usal.es:10366/1336352025-04-30T21:03:45Zcom_10366_133431com_10366_122682com_10366_4666com_10366_3823col_10366_133432

Blanco Valencia, Xiomara Patricia Becerra, M. A. Castro Ospina, A. E. Ortega Adarme, M. Viveros Melo, D. Peluffo-Ordóñez, Diego H. 2017-07-26T11:08:06Z 2017-07-26T11:08:06Z 2017-01-12 ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 6 (2017) 2255-2863 http://hdl.handle.net/10366/133635 This work outlines a unified formulation to represent spectral approaches for both dimensionality reduction and clustering. Proposed formulation starts with a generic latent variable model in terms of the projected input data matrix.Particularly, such a projection maps data onto a unknown high-dimensional space. Regarding this model, a generalized optimization problem is stated using quadratic formulations and a least-squares support vector machine.The solution of the optimization is addressed through a primal-dual scheme.Once latent variables and parameters are determined, the resultant model outputs a versatile projected matrix able to represent data in a low-dimensional space, as well as to provide information about clusters. Particularly, proposedformulation yields solutions for kernel spectral clustering and weighted-kernel principal component analysis. eng https://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivs 3.0 Unported Computación Informótica Computing Information Technology Kernel-based framework for spectral dimensionality reduction and clustering formulation: A theoretical study info:eu-repo/semantics/article