Multiple Kernel Learning for Spectral Dimensionality Reduction

Autor:	Diego Hernán Peluffo-Ordóñez, Juan C. Alvarado-Pérez, Andrés Eduardo Castro-Ospina, Edgardo Javier Revelo-Fuelagán
Rok vydání:	2015
Předmět:	Multiple kernel learning Kernel method Polynomial kernel Computer science business.industry Kernel (statistics) Dimensionality reduction Radial basis function kernel Pattern recognition Artificial intelligence Linear combination business Kernel principal component analysis
Zdroj:	Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications ISBN: 9783319257501 CIARP
DOI:	10.1007/978-3-319-25751-8_75
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4e4f7565eb087e19a5533728c428fd7d https://doi.org/10.1007/978-3-319-25751-8_75 Zobrazit plný text záznamu