Collaborative and geometric multi-kernel learning for multi-class classification

Abstract

The multi-class classification is the problem of classifying the sample into one of three or more classes. In this paper, we propose an algorithm named collaborative and geometric multi-kernel learning (CGMKL) to classify multi-class data into corresponding class directly. The CGMKL uses the Multiple Empirical Kernel Learning (MEKL) to map the sample into multiple kernel spaces, and then trains the softmax function in each kernel space. To realize the collaborative learning, one regularization term, which controls the consistent outputs of samples in different kernel spaces, provides the complementary information. Moreover, another regularization term exhibits the classification result with a geometric feature by reducing the within-class distance of the outputs of samples. Extensive Experiments on the multi-class data sets validate the effectiveness of the CGMKL.