Online Multi-Kernel Learning with Orthogonal Random Features

Kernel-based methods have well-appreciated performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. To cope with this limitation, multi-kernel learning has gained popularity thanks to its flexibility in choosing kernels from a prescribed kernel dictionary. Leveraging the random feature approximation and its recent orthogonality-promoting variant, the present contribution develops an online multi-kernel learning scheme to infer the intended nonlinear function 'on the fly.' Performance analysis shows that the novel algorithm can afford sublinear regret. Numerical tests on real datasets are carried out to showcase the effectiveness of the proposed algorithms.