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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 41–50 of 158 papers

TitleStatusHype
Spectal Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning—0
Disjunctive Branch-And-Bound for Certifiably Optimal Low-Rank Matrix Completion—0
A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion—0
Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time—0
Learning Transition Operators From Sparse Space-Time Samples—0
Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity—0
Online Low Rank Matrix Completion—0
Introducing the Huber mechanism for differentially private low-rank matrix completion—0
Robust Matrix Completion with Heavy-tailed Noise—0
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference—0
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