Metric based Gaussian kernel learning for classification

Metric learning for KNN has attracted increasing attentions in the field of machine learning (e.g., based on the parametric form of Mahalanobis distance). A good distance metric is also the foundation for other machine learning models, for example, a Gaussian RBF kernel is constructed upon distance metric defined in the feature vector space. However, besides the KNN classifier, there is little research work on learning a good distancemetric for distance-basedmodels. In this paper, we propose a novel algorithmto learn aMahalanobis-distance type metric for Gaussian RBF kernels. We conduct experiments on 5 data sets from the UCI Machine Learning Repository database and two face recognition data sets. The classification results show that the proposed algorithm can outperform other state-of-arts on most of the data sets and achieve comparable results on the rest of data sets.