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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Multiple Kernel Learning for Spectral Dimensionality Reduction
verfasst von : Diego Hernán Peluffo-Ordóñez, Andrés Eduardo Castro-Ospina, Juan Carlos Alvarado-Pérez, Edgardo Javier Revelo-Fuelagán
Erschienen in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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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.