About

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

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Greatest papers with code

Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport

18 Feb 2017hiroyuki-kasai/RSOpt

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

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

20 Apr 2016xuehy/depthInpainting

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

27 Oct 2019cbig-iowa/giraf

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

4 Apr 2014jasonsun0310/MatrixCompletion.jl

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

LOW-RANK MATRIX COMPLETION

Collaborative Filtering with Graph Information: Consistency and Scalable Methods

NeurIPS 2015 rofuyu/exp-grmf-nips15

Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space.

 Ranked #1 on Recommendation Systems on Flixster (using extra training data)

LOW-RANK MATRIX COMPLETION RECOMMENDATION SYSTEMS

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

7 Dec 2019anikpram/Deep-SLR

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

22 Apr 2019mohit-shrma/matfac

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.

LOW-RANK MATRIX COMPLETION RECOMMENDATION SYSTEMS

Provable Subspace Tracking from Missing Data and Matrix Completion

6 Oct 2018vdaneshpajooh/NORST-rmc

In this work, we show that a simple modification of our robust ST solution also provably solves ST-miss and robust ST-miss.

LOW-RANK MATRIX COMPLETION

A Gradient Descent Algorithm on the Grassman Manifold for Matrix Completion

27 Oct 2009strin/pyOptSpace

We consider the problem of reconstructing a low-rank matrix from a small subset of its entries.

LOW-RANK MATRIX COMPLETION