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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 26–50 of 158 papers

TitleStatusHype
Bayesian Learning for Low-Rank matrix reconstruction—0
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference—0
Bounded Manifold Completion—0
Spectal Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning—0
Coherence and sufficient sampling densities for reconstruction in compressed sensing—0
Efficiently escaping saddle points on manifolds—0
Entry-Specific Bounds for Low-Rank Matrix Completion under Highly Non-Uniform Sampling—0
An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion—0
Decentralized Singular Value Decomposition for Large-scale Distributed Sensor Networks—0
Low-rank matrix completion theory via Plucker coordinates—0
Advancing Matrix Completion by Modeling Extra Structures beyond Low-Rankness—0
Deep learned SVT: Unrolling singular value thresholding to obtain better MSE—0
Depth Enhancement via Low-rank Matrix Completion—0
A Pre-training Oracle for Predicting Distances in Social Networks—0
Depth Restoration: A fast low-rank matrix completion via dual-graph regularization—0
Discrete Aware Matrix Completion via Convexized _0-Norm Approximation—0
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
Data-based system representations from irregularly measured data—0
Effect of Beampattern on Matrix Completion with Sparse Arrays—0
Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation—0
A Riemannian gossip approach to subspace learning on Grassmann manifold—0
A New Retraction for Accelerating the Riemannian Three-Factor Low-Rank Matrix Completion Algorithm—0
Communication Efficient Parallel Algorithms for Optimization on Manifolds—0
Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction—0
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion—0
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