Learning Data-adaptive Nonparametric Kernels
share
Traditional kernels or their combinations are often not sufficiently flexible to fit the data in complicated practical tasks. In this paper, we present a Data-Adaptive Nonparametric Kernel (DANK) learning framework by imposing an adaptive matrix on the kernel/Gram matrix in an entry-wise strategy. Since we do not specify the formulation of the adaptive matrix, each entry in it can be directly and flexibly learned from the data. Therefore, the solution space of the learned kernel is largely expanded, which makes DANK flexible to adapt to the data. Specifically, the proposed kernel learning framework can be seamlessly embedded to support vector machines (SVM) and support vector regression (SVR), which has the capability of enlarging the margin between classes and reducing the model generalization error. Theoretically, we demonstrate that the objective function of our devised model is gradient-Lipschitz continuous. Thereby, the training process for kernel and parameter learning in SVM/SVR can be efficiently optimized in a unified framework. Further, to address the scalability issue in DANK, a decomposition-based scalable approach is developed, of which the effectiveness is demonstrated by both empirical studies and theoretical guarantees. Experimentally, our method outperforms other representative kernel learning based algorithms on various classification and regression benchmark datasets.
READ FULL TEXT