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Multiple kernel learning for spectral dimensionality reduction

Peluffo-Ordóñez, Diego H. Y Revelo-Fuelagán, Edgardo J Y Castro-Ospina, Andrés Y Alvarado, Juan C. (2016) Multiple kernel learning for spectral dimensionality reduction. Lecture Notes In Computer Science, 9423. 626 -634. ISSN 0302-9743

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URL Oficial: https://link.springer.com/chapter/10.1007/978-3-31...

Resumen

This work introduces a multiple kernel learning (MKL) approach for selecting and combining different spectral methods of dimensionality reduction (DR). From a predefined set of kernels representing conventional spectral DR methods, a generalized kernel is calculated by means of a linear combination of kernel matrices. Coefficients are estimated via a variable ranking aimed at quantifying how much each variable contributes to optimize a variance preservation criterion. All considered kernels are tested within a kernel PCA framework. The experiments are carried out over well-known real and artificial data sets. The performance of compared DR approaches is quantified by a scaled version of the average agreement rate between K-ary neighborhoods. Proposed MKL approach exploits the representation ability of every single method to reach a better embedded data for both getting more intelligible visualization and preserving the structure of data.

Tipo de Elemento: Artículo
Palabras Clave: Dimensionality reduction, Generalized kernel, Kernel PCA, Multiple kernel learning
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 04:48
Ultima Modificación: 28 Jun 2018 04:48
URI: http://sired.udenar.edu.co/id/eprint/4718

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