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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 61–70 of 158 papers

TitleStatusHype
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
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion—0
Data-based system representations from irregularly measured data—0
Harmonic Retrieval Using Weighted Lifted-Structure Low-Rank Matrix Completion—0
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