Kernel Principal Component Analysis (KPCA) is a nonlinear feature extraction approach, which generally needs to eigen-decompose the kernel matrix. But the size of kernel matrix scales with the number ...

When debugging something as involved as kernel scheduler timings, you would typically use one of the software-based debugging mechanisms available. However, in cases when software is close to bare ...

The Kernel k-Means algorithm for clustering extends the classic k-Means clustering algorithm. It uses the kernel trick to implicitly calculate distances on a higher dimensional space, thus overcoming ...