Kronecker product is a powerful operation in matrix computations. This paper presents a
technique called multiple Kronecker product sum (or shortly MKPS) approximation. We show …

In this paper, we present a class of decomposition techniques for data represented as
matrices. The main idea is to transform a matrix into a sequence of components in order to …

Singular value decompositions are a particularly attractive matrix factorization for ill-posed
problems because singular value magnitudes reveal information about the relative …

The matrix decomposition introduced in this chapter is very important in many practical
applications, since it yields the best possible approximation (in a certain sense) of a given …

Many image processing applications require computing approximate solutions of very large,
ill-conditioned linear systems. Physical assumptions of the imaging system usually dictate …

Image restoration is one of many applications in which a solution to Kf= g, the classic linear
algebra problem, is sought. Many methods exist for approximating the solution in the …

Considering that data used in many applications are intrinsically in matrix form rather than in
vector form, this paper focuses on the generalized version of the problem of a low-rank …

Efficient computation of a matrix-matrix kernel. Page 1 Efficient computation of a matrix-matrix
kernel. Iain S. Duff and Chiara Puglisi CERFACS, 42 Av. G. Coriolis, 31057 Toulouse CEDEX …

This thesis starts with the fundamentals of matrix theory and ends with applications of the
matrix singular value decomposition (SVD). The background matrix theory coverage …

Image Compression using Singular Value Decomposition Page 1 1/41 Applications of Linear
Algebra Image Compression using Singular Value Decomposition David Richards and Adam …