Learning Anisotropic RBF Kernels

We present an approach for learning an anisotropic RBF kernel in a game theoretical setting where the value of the game is the degree of separation between positive and negative training examples. The method extends a previously proposed method (KOMD) to perform feature re-weighting and distance metric learning in a kernel-based classification setting. Experiments on several benchmark datasets demonstrate that our method generally outperforms state-of-the-art distance metric learning methods, including the Large Margin Nearest Neighbor Classification family of methods.