Matrix completion models have been receiving keen attention due to their wide applications
in science and engineering. However, the majority of these models assumes a symmetric …

The problem of completing high-dimensional matrices from a limited set of observations
arises in many big data applications, especially recommender systems. The existing matrix …

Matrix completion has received a vast amount of attention and research due to its wide
applications in various study fields. Existing methods of matrix completion consider only …

Conventional matrix completion methods approximate the missing values by assuming the
matrix to be low-rank, which leads to a linear approximation of missing values. It has been …

Nonlinear hyperspectral unmixing has recently received considerable attention, as linear
mixture models do not lead to an acceptable resolution in some problems. In fact, most …

In this work, we formulate the image in-painting as a matrix completion problem. Traditional
matrix completion methods are generally based on linear models, assuming that the matrix …

Дисертацію присвячено розробці методів та процесорів розпізнавання багатомірних
образів у Хеммінговому просторі. Розроблений метод оцінки Хеммінгової віддалі на …

In this study we propose a Deep structured LOw Rank Matrix Approximation model
(DLORMA) that incorporates additional stacked denoising autoencoders and local matrix …