Low-Rank Matrix Completion

16 papers with code • 0 benchmarks • 0 datasets

Low-Rank Matrix Completion is an important problem with several applications in areas such as recommendation systems, sketching, and quantum tomography. The goal in matrix completion is to recover a low rank matrix, given a small number of entries of the matrix.

Source: Universal Matrix Completion


Greatest papers with code

Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport

hiroyuki-kasai/RSOpt 18 Feb 2017

In recent years, stochastic variance reduction algorithms have attracted considerable attention for minimizing the average of a large but finite number of loss functions.

Low-Rank Matrix Completion

Riemannian stochastic variance reduced gradient on Grassmann manifold

hiroyuki-kasai/RSOpt 24 May 2016

In this paper, we propose a novel Riemannian extension of the Euclidean stochastic variance reduced gradient algorithm (R-SVRG) to a compact manifold search space.

Low-Rank Matrix Completion Translation

Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient Regularization

xuehy/depthInpainting 20 Apr 2016

The proposed low gradient regularization is integrated with the low rank regularization into the low rank low gradient approach for depth image inpainting.

Image Inpainting Low-Rank Matrix Completion

Structured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine Learning

cbig-iowa/giraf 27 Oct 2019

In this survey, we provide a detailed review of recent advances in the recovery of continuous domain multidimensional signals from their few non-uniform (multichannel) measurements using structured low-rank matrix completion formulation.

Low-Rank Matrix Completion

Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion

jasonsun0310/MatrixCompletion.jl 4 Apr 2014

Numerical results show that our proposed algorithm is more efficient than competing algorithms while achieving similar or better prediction performance.

Low-Rank Matrix Completion

Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)

anikpram/Deep-SLR 7 Dec 2019

The main challenge with this strategy is the high computational complexity of matrix completion.

Image Reconstruction Low-Rank Matrix Completion

Adaptive Matrix Completion for the Users and the Items in Tail

mohit-shrma/matfac 22 Apr 2019

In this work, we show that the skewed distribution of ratings in the user-item rating matrix of real-world datasets affects the accuracy of matrix-completion-based approaches.

Collaborative Filtering Low-Rank Matrix Completion +1

Guaranteed Rank Minimization via Singular Value Projection

HauLiang/Matrix-Completion-Methods NeurIPS 2010

Minimizing the rank of a matrix subject to affine constraints is a fundamental problem with many important applications in machine learning and statistics.

Low-Rank Matrix Completion

A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples

ckuemmerle/MatrixIRLS 3 Jun 2021

We propose an iterative algorithm for low-rank matrix completion that can be interpreted as an iteratively reweighted least squares (IRLS) algorithm, a saddle-escaping smoothing Newton method or a variable metric proximal gradient method applied to a non-convex rank surrogate.

Low-Rank Matrix Completion