PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Supervised and Localized Dimensionality Reduction from Multiple Feature Representations or Kernels
Mehmet Gönen and Ethem Alpaydın
In: NIPS 2010 Workshop: New Directions in Multiple Kernel Learning, 11 Dec 2010, Whistler, Canada.


We propose a supervised and localized dimensionality reduction method that combines multiple feature representations or kernels. Each feature representation or kernel is used where it is suitable through a parametric gating model in a supervised manner for efficient dimensionality reduction and classification, and local projection matrices are learned for each feature representation or kernel. The kernel machine parameters, the local projection matrices, and the gating model parameters are optimized using an alternating optimization procedure composed of kernel machine training and gradient-descent updates. Empirical results on benchmark data sets validate the method in terms of classification accuracy, smoothness of the solution, and ease of visualization.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7720
Deposited By:Mehmet Gönen
Deposited On:17 March 2011