Kernel-based framework for spectral dimensionality reduction and clustering formulation: A theoretical study

Kernel-based framework for spectral dimensionality reduction and clustering formulation: A theoretical study

<p>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.<br />Particularly, such a projection maps data onto...

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Bibliographic Details
Main Authors: Xiomara Patricia BLANCO VALENCIA, M. A. BECERRA, A. E. CASTRO OSPINA, M. ORTEGA ADARME, D. VIVEROS MELO, D. H. PELUFFO ORDÓÑEZ
Format: Article
Language:English
Published: Ediciones Universidad de Salamanca 2017-03-01
Series:Advances in Distributed Computing and Artificial Intelligence Journal
Subjects:
cluster
dimension reduction
support vector machine
low-dimensional space
Online Access:https://revistas.usal.es/index.php/2255-2863/article/view/15243

Internet

https://revistas.usal.es/index.php/2255-2863/article/view/15243

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