Kernel-based framework for spectral dimensionality reduction and clustering formulation: A theoretical study. BLANCO VALENCIA, X., P., BECERRA, M., A., CASTRO OSPINA, A., E., ORTEGA ADARME, M., VIVEROS MELO, D., & PELUFFO ORDÓÑEZ, D., H. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 6(1):31, 1, 2017. ![Kernel-based framework for spectral dimensionality reduction and clustering formulation: A theoretical study [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website abstract bibtex See, stats, and : https :// www. researchgate. net / publication/ 315475649 Kernel-based dimensionality formulation: A Article DOI : 10 . 14201 / ADCAIJ2017613140 CITATIONS 0 READS 57 6 , including : Some : DATA Case (CBR) for Xiomara Universidad 8 SEE Miguel Institución 35 SEE A . E . Castro - Ospina Instituto 26 SEE Diego Universidad 136 SEE All . The . KEYWORD ABSTRACT Kernel PCA ; Spectral clustering ; Support vector machine . 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 mod - el , 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 , proposed formulation yields solutions for kernel spectral clustering and weighted - ker - nel principal component analysis .
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