Automated Spectral Kernel Learning

Understand

The generalization performance of kernel methods is largely determined by the kernel, but common kernels are stationary thus input-independent and output-independent, that limits their applications on complicated tasks.

• In this paper, we propose a powerful and efficient spectral kernel learning framework and learned kernels are dependent on both inputs and outputs, by using non-stationary spectral kernels and flexibly learning the spectral measure from the data.
• Further, we derive a data-dependent generalization error bound based on Rademacher complexity, which estimates the generalization ability of the learning framework and suggests two regularization terms to improve performance.
• Extensive experimental results validate the effectiveness of the proposed algorithm and confirm our theoretical results.