Results 31 to 40 of about 3,678,430 (211)
Deep Matrix Factorization Based on Convolutional Neural Networks for Image Inpainting
Entropy, 2022 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 is low rank.
Xiaoxuan Ma, Zhiwen Li, Hengyou Wang
doaj +1 more source
The Power of Convex Relaxation: Near-Optimal Matrix Completion [PDF]
IEEE Transactions on Information Theory, 2009 This paper is concerned with the problem of recovering an unknown matrix from a small fraction of its entries. This is known as the matrix completion problem, and comes up in a great number of applications, including the famous Netflix Prize and other ...
E. Candès, T. Tao
semanticscholar +1 more source
IEEE Access, 2022 Recent advances have shown that the challenging problem of matrix completion arises from real-world applications, such as image recovery, and recommendation systems.
Ying Zhang +3 more
doaj +1 more source
A Riemannian rank-adaptive method for low-rank matrix completion [PDF]
Computational optimization and applications, 2021 The low-rank matrix completion problem can be solved by Riemannian optimization on a fixed-rank manifold. However, a drawback of the known approaches is that the rank parameter has to be fixed a priori. In this paper, we consider the optimization problem
Bin Gao, P. Absil
semanticscholar +1 more source
Matrix completion via modified schatten 2/3-norm
EURASIP Journal on Advances in Signal Processing, 2023 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.
Jincai Ha +3 more
doaj +1 more source
Robust matrix completion [PDF]
Probability Theory and Related Fields, 2016 This paper considers the problem of recovery of a low-rank matrix in the situation when most of its entries are not observed and a fraction of observed entries are corrupted. The observations are noisy realizations of the sum of a low rank matrix, which we wish to recover, with a second matrix having a complementary sparse structure such as element ...
Klopp, Olga +2 more
openaire +3 more sources
Online Low Rank Matrix Completion [PDF]
International Conference on Learning Representations, 2022 We study the problem of {\em online} low-rank matrix completion with $\mathsf{M}$ users, $\mathsf{N}$ items and $\mathsf{T}$ rounds. In each round, the algorithm recommends one item per user, for which it gets a (noisy) reward sampled from a low-rank ...
Prateek Jain, S. Pal
semanticscholar +1 more source
A Novel Hierarchical Deep Matrix Completion Method
IEEE Access, 2021 The matrix completion technique based on matrix factorization for recovering missing items is widely used in collaborative filtering, image restoration, and other applications.
Yaru Chen +7 more
doaj +1 more source
, 2022 The matrix completion problem aims to reconstruct a low-rank matrix based on a revealed set of possibly noisy entries. Prior works consider completing the entire matrix with generalization error guarantees. However, the completion accuracy can be drastically different over different entries.
Hazan, E +4 more
openaire +3 more sources
Inductive Matrix Completion Using Graph Autoencoder [PDF]
International Conference on Information and Knowledge Management, 2021 Recently, the graph neural network (GNN) has shown great power in matrix completion by formulating a rating matrix as a bipartite graph and then predicting the link between the corresponding user and item nodes.
Wei Shen +6 more
semanticscholar +1 more source