We propose an efficient algorithm for principal component analysis (PCA) that is applicable when only the inner product with a given vector is needed. We show that Krylov subspace...
This paper examines the problem of learning with a finite and possibly large set of p base kernels. It presents a theoretical and empirical analysis of an approach addressing thi...
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...
Kernel Principal Component Analysis (KPCA) is investigated for feature extraction from hyperspectral remotesensing data. Features extracted using KPCA are used to construct the Ex...
Mathieu Fauvel, Jocelyn Chanussot, Jon Atli Benedi...
We derive a family of kernels on dynamical systems by applying the Binet-Cauchy theorem to trajectories of states. Our derivation provides a unifying framework for all kernels on d...
S. V. N. Vishwanathan, Alexander J. Smola, Ren&eac...