In this paper, we propose a novel method, called local nonnegative matrix factorization (LNMF), for learning spatially localized, parts-based subspace representation of visual pat...
Stan Z. Li, XinWen Hou, HongJiang Zhang, QianSheng...
In this paper, we consider some matrix subgroups of the general linear group and in particular the special linear group that are defined by a quadratic matrix identity. The Lie al...
Anthony M. Bloch, Peter E. Crouch, Jerrold E. Mars...
Abstract. We present a relational algebra based framework for compiling e cient sparse matrix code from dense DO-ANY loops and a speci cation of the representation of the sparse ma...
Recently Non-negative Matrix Factorization (NMF) has received a lot of attentions in information retrieval, computer vision and pattern recognition. NMF aims to find two non-nega...
There is a class of sparse matrix computations, such as direct solvers of systems of linear equations, that change the fill-in (nonzero entries) of the coefficient matrix, and invo...