Kernel Principal Component Analysis extends linear PCA from a Euclidean space to any reproducing kernel Hilbert space. Robustness issues for Kernel PCA are studied. The sensitivit...
Dynamic kernel memory has been a popular target of recent kernel malware due to the difficulty of determining the status of volatile dynamic kernel objects. Some existing approach...
Junghwan Rhee, Ryan Riley, Dongyan Xu, Xuxian Jian...
We present a novel approach to the problem of detection of visual similarity between a template image, and patches in a given image. The method is based on the computation of a lo...
Abstract. The Gram matrix plays a central role in many kernel methods. Knowledge about the distribution of eigenvalues of the Gram matrix is useful for developing appropriate model...
—As process variations become a significant problem in deep sub-micron technology, a shift from deterministic static timing analysis to statistical static timing analysis for hig...