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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 81–90 of 158 papers

TitleStatusHype
Graph-Based Matrix Completion Applied to Weather Data—0
Guaranteed Matrix Completion Under Multiple Linear Transformations—0
Harmonic Retrieval Using Weighted Lifted-Structure Low-Rank Matrix Completion—0
High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization—0
High-Rank Matrix Completion and Clustering under Self-Expressive Models—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion—0
Introducing the Huber mechanism for differentially private low-rank matrix completion—0
Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion—0
Learning Transition Operators From Sparse Space-Time Samples—0
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