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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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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