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.