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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 151–158 of 158 papers

TitleStatusHype
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
A Riemannian gossip approach to subspace learning on Grassmann manifold—0
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features—0
Background Subtraction via Fast Robust Matrix Completion—0
Bayesian Learning for Low-Rank matrix reconstruction—0
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference—0
Bounded Manifold Completion—0
Spectal Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning—0
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