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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 11–20 of 158 papers

TitleStatusHype
A divide-and-conquer algorithm for binary matrix completion—0
Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees—0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion—0
Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction—0
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM—0
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
Adaptive Noisy Matrix Completion—0
A Pre-training Oracle for Predicting Distances in Social Networks—0
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion—0
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