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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 51–75 of 158 papers

TitleStatusHype
Efficiently escaping saddle points on manifolds—0
Graph-Based Matrix Completion Applied to Weather Data—0
Entry-Specific Bounds for Low-Rank Matrix Completion under Highly Non-Uniform Sampling—0
Errata: Distant Supervision for Relation Extraction with Matrix Completion—0
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion—0
Exact Linear Convergence Rate Analysis for Low-Rank Symmetric Matrix Completion via Gradient Descent—0
Exact Reconstruction of Euclidean Distance Geometry Problem Using Low-rank Matrix Completion—0
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery—0
Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size—0
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models—0
Fixed-rank matrix factorizations and Riemannian low-rank optimization—0
Communication Efficient Parallel Algorithms for Optimization on Manifolds—0
Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion—0
Generalized Low-Rank Matrix Completion Model with Overlapping Group Error Representation—0
Fusion Subspace Clustering: Full and Incomplete Data—0
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion—0
Guaranteed Matrix Completion Under Multiple Linear Transformations—0
Data-based system representations from irregularly measured data—0
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion—0
Harmonic Retrieval Using Weighted Lifted-Structure Low-Rank Matrix Completion—0
High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization—0
Introducing the Huber mechanism for differentially private low-rank matrix completion—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution—0
Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion—0
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