Data-dependent multiple kernel learning algorithm based on soft-grouping

Abstract

Multiple kernel learning strategy has emerged as a powerful tool because it can easily combine information from multiple data sources. However, learning an optimal kernel is still a challenging work and need to be further researched. In this paper, we propose a data-dependent multiple kernel learning algorithm based on soft-grouping (SC-DMKL). The core ideas of the SC-DMKL are twofold: (1) we take a soft-group process on the training samples to accommodate the correlation and the diversity of the samples; (2) alternatively optimize the kernel weights and the classifier to adaptively learn a data-dependent composite kernel. The final composite kernel is determined by the probability of samples falling to the groups and the kernel weights of these groups. Therefore, our method is actually a sample-specific MKL method with a soft restriction on the kernel weights. This restriction is actually the representation of the correlation of samples. The experiments on the synthetic dataset indicate that the kernel weights solved by our algorithm are more suitable for the characteristics of the datasets and the experiments on the real world datasets verify that the classification accuracies are improved.