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]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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 title = {Kernel-based framework for spectral dimensionality reduction and clustering formulation: A theoretical study},
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 year = {2017},
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 abstract = {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 .},
 bibtype = {article},
 author = {BLANCO VALENCIA, Xiomara Patricia and BECERRA, M. A. and CASTRO OSPINA, A. E. and ORTEGA ADARME, M. and VIVEROS MELO, D. and PELUFFO ORDÓÑEZ, D. H.},
 journal = {ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal},
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}

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