An element-wise kernel learning framework

An effective kernel learning framework is a fundamental issue which has been attracted considerable attention during the past decade. However, existing multiple kernel learning algorithms follow the assumption that an optimal kernel is a weighted combination of pre-specified kernels, leading to limited kernel representation and insufficient flexibility. Moreover, data-dependent kernel learning approaches explore a flexible kernel matrix in the neighborhood area of the fixed initial kernel matrix, resulting in the restriction on the kernel search space. To solve these limitations, we propose element-wise kernel learning via the connection between representative kernel learning and parameter-free kernel learning. A data-adaptive kernel matrix without any specific formulations is imposed on the representative kernels in an element-wise manner. To diminish the adverse effect of the correlated information among pre-specified base kernels, representative kernels are diversely determined by the kernel selection process. The extensive experiments on benchmark and real-world datasets indicate our proposed framework achieves superior performance against well-known kernel-based algorithms.