Joint Learning of Distance Metric and Kernel Classifier via Multiple Kernel Learning

Both multiple kernel learning (MKL) and support vector metric learning (SVML) were developed to adaptively learn kernel function from training data, and have been proved to be effective in many challenging applications. Actually, many MKL formulations are based on either the max-margin or radius-margin based principles, which in spirit is consistent with the optimization principle of the between/within class distances adopted in SVML. This motivates us to investigate their connection and develop a novel model for joint learning distance metric and kernel classifier. In this paper, we provide a new parameterization scheme for incorporating the squared Mahalanobis distance into the Gaussian RBF kernel, and formulate kernel learning into a GMKL framework. Moreover, radius information is also incorporated as the supplement for considering the within-class distance in the feature space. We demonstrate the effectiveness of the proposed algorithm on several benchmark datasets of varying sizes and difficulties. Experimental results show that the proposed algorithm achieves competitive classification accuracies with both state-of-the-art metric learning models and representative kernel learning models.