Low-Rank Matrix Completion
25 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
Benchmarks
These leaderboards are used to track progress in Low-Rank Matrix Completion
Most implemented papers
Provable Subspace Tracking from Missing Data and Matrix Completion
In this work, we show that a simple modification of our robust ST solution also provably solves ST-miss and robust ST-miss.
Adaptive Matrix Completion for the Users and the Items in Tail
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.
Structured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine Learning
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.
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)
The main challenge with this strategy is the high computational complexity of matrix completion.
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method
We propose an iterative algorithm for low-rank matrix completion that can be interpreted as both an iteratively reweighted least squares (IRLS) algorithm and a saddle-escaping smoothing Newton method applied to a non-convex rank surrogate objective.
Mixed Membership Graph Clustering via Systematic Edge Query
This work aims at learning mixed membership of nodes using queried edges.
Simulation comparisons between Bayesian and de-biased estimators in low-rank matrix completion
In this paper, we study the low-rank matrix completion problem, a class of machine learning problems, that aims at the prediction of missing entries in a partially observed matrix.
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples
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.
GNMR: A provable one-line algorithm for low rank matrix recovery
Low rank matrix recovery problems, including matrix completion and matrix sensing, appear in a broad range of applications.
Generalized Nonconvex Approach for Low-Tubal-Rank Tensor Recovery
The tensor-tensor product-induced tensor nuclear norm (t-TNN) (Lu et al., 2020) minimization for low-tubal-rank tensor recovery attracts broad attention recently.
