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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 21–30 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
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion—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
Abrupt Learning in Transformers: A Case Study on Matrix Completion—0
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