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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 71–80 of 158 papers

TitleStatusHype
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
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
Fusion Subspace Clustering: Full and Incomplete Data—0
Generalized Low-Rank Matrix Completion Model with Overlapping Group Error Representation—0
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