Low-rank matrix decompositions are essential tools in the application of kernel methods to large-scale learning problems. These decompositions have generally been treated as black...
Non-negative matrix factorization (NMF) provides a lower rank approximation of a matrix. Due to nonnegativity imposed on the factors, it gives a latent structure that is often mor...
— This paper focuses on controlling absorbing sets for a class of regular LDPC codes, known as separable, circulantbased (SCB) codes. For a specified circulant matrix, SCB codes...
We address the problem of estimating a fundamental matrix from a given set of corresponding pixels in two perspective images of a 3D scene that form a stereopair. The 3x3 fundamen...
In this study, nonnegative matrix factorization is recast as the problem of approximating a polytope on the probability simplex by another polytope with fewer facets. Working on th...