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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 101–110 of 158 papers

TitleStatusHype
Nearly Optimal Robust Matrix Completion—0
Reflection Removal Using Low-Rank Matrix Completion—0
A Riemannian gossip approach to subspace learning on Grassmann manifold—0
Recovery of damped exponentials using structured low rank matrix completion—0
Novel Structured Low-rank algorithm to recover spatially smooth exponential image time series—0
Algebraic Variety Models for High-Rank Matrix CompletionCode0
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis—0
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM—0
Riemannian stochastic variance reduced gradient algorithm with retraction and vector transportCode0
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability—0
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