Learning the Kernel Function via Regularization

Generalized Multiple Kernel Learning (GMKL) has been proposed in the literature for feature selection. GMKL learns linear, product and exponential combinations of given base kernels which makes it more robust and efficient than traditional Multiple ...

Regularized Kernel Discriminant Analysis (RKDA) performs linear discriminant analysis in the feature space via the kernel trick. The performance of RKDA depends on the selection of kernels. In this paper, we consider the problem of learning an optimal ...

Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in ...