Low-rank matrix completion is a hot topic in the field of machine learning. It is widely used in
image processing, recommendation systems and subspace clustering. However, the …

Digital image processing remains a challenging domain of programming for several
reasons. First the issue of digital image processing appeared relatively late in computer …

NOTE Relaxation: Application to the Matrix Reconstruction Problem Page 1 COMPUTER
OIUPHICS AND IMAOE PROCBSSINO 15, 288-295 (198 1) NOTE Relaxation: Application to …

A wide variety of matrix transforms have been used for compression of image and video
data. Transforms have also been used for motion estimation, quantization, and image …

Low-rank matrix completion is an important research topic with a wide range of applications.
One prevailing way for matrix recovery is based on rank minimization. Directly solving this …

We consider the matrix completion problem that aims to construct a low rank matrix X that
approximates a given large matrix Y from partially known sample data in Y. In this paper we …

Let A be an m-by-n matrix with m= m1m2 and n= n1n2. We consider the problem of finding
(mathematical formula omitted) so that (mathematical formula omitted) is minimized. This …

In this paper, we consider the problem of recovering an unknown sparse matrix X from the
matrix sketch Y= AX B^ T. The dimension of Y is less than that of X, and A and B are known …

Matrix completion is critical in a wide range of scientific and engineering applications, such
as image restoration and recommendation systems. This topic is commonly expressed as a …

Commutative quaternion matrices have a wide range of applications in signal and image
processing, face recognition, neural networks, etc., and matrix decomposition occupies an …