Supervised and Localized Dimensionality Reduction from Multiple Feature Representations or Kernels

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.