In this paper we present a new incomplete factorization of a square matrix into triangular factors in which we get standard LU or LDLT factors (direct factors) and their inverses (...
— We consider memory subsystem optimizations for improving the performance of sparse scientific computation while reducing the power consumed by the CPU and memory. We first co...
In our previous work, we have developed sparse least squares support vector regressors (sparse LS SVRs) trained in the primal form in the reduced empirical feature space. In this p...
It has been of great interest to find sparse and/or nonnegative representations in computer vision literature. In this paper we propose a novel method to such a purpose and refer...
We introduce the posterior probabilistic clustering (PPC), which provides a rigorous posterior probability interpretation for Nonnegative Matrix Factorization (NMF) and removes th...