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Low-Rank Matrix Completion

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

Papers

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Showing 101–125 of 158 papers

TitleStatusHype
Nearly Optimal Robust Matrix Completion—0
Reflection Removal Using Low-Rank Matrix Completion—0
A Riemannian gossip approach to subspace learning on Grassmann manifold—0
Recovery of damped exponentials using structured low rank matrix completion—0
Novel Structured Low-rank algorithm to recover spatially smooth exponential image time series—0
Algebraic Variety Models for High-Rank Matrix CompletionCode0
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis—0
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM—0
Riemannian stochastic variance reduced gradient algorithm with retraction and vector transportCode0
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability—0
Modelling Competitive Sports: Bradley-Terry-Élő Models for Supervised and On-Line Learning of Paired Competition Outcomes—0
High-Rank Matrix Completion and Clustering under Self-Expressive Models—0
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features—0
Low-tubal-rank Tensor Completion using Alternating Minimization—0
Nearly-optimal Robust Matrix Completion—0
Riemannian stochastic variance reduced gradient on Grassmann manifoldCode0
Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient RegularizationCode0
Scaled stochastic gradient descent for low-rank matrix completion—0
Secrets of Matrix Factorization: Approximations, Numerics, Manifold Optimization and Random Restarts—0
Collaborative Filtering with Graph Information: Consistency and Scalable MethodsCode0
An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion—0
Symmetric Tensor Completion from Multilinear Entries and Learning Product Mixtures over the Hypercube—0
A New Retraction for Accelerating the Riemannian Three-Factor Low-Rank Matrix Completion Algorithm—0
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion—0
Relaxed Leverage Sampling for Low-rank Matrix Completion—0
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