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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 31–40 of 158 papers

TitleStatusHype
Bounded Manifold Completion—0
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference—0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
A divide-and-conquer algorithm for binary matrix completion—0
Bayesian Learning for Low-Rank matrix reconstruction—0
Background Subtraction via Fast Robust Matrix Completion—0
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features—0
Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction—0
Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees—0
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM—0
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