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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 81–90 of 158 papers

TitleStatusHype
Adaptive Matrix Completion for the Users and the Items in TailCode0
Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation—0
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization—0
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion—0
Communication Efficient Parallel Algorithms for Optimization on Manifolds—0
Provable Subspace Tracking from Missing Data and Matrix CompletionCode0
Fusion Subspace Clustering: Full and Incomplete Data—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion—0
Ranking Recovery from Limited Comparisons using Low-Rank Matrix Completion—0
Sparse Group Inductive Matrix Completion—0
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