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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 121–130 of 158 papers

TitleStatusHype
Symmetric Matrix Completion with ReLU Sampling—0
Symmetric Tensor Completion from Multilinear Entries and Learning Product Mixtures over the Hypercube—0
Tensor Methods for Nonlinear Matrix Completion—0
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion—0
Leave-one-out Approach for Matrix Completion: Primal and Dual Analysis—0
The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion—0
Truncated Matrix Completion - An Empirical Study—0
Uncertainty Quantification For Low-Rank Matrix Completion With Heterogeneous and Sub-Exponential Noise—0
Universal Matrix Completion—0
Unsupervised Spectral Learning of WCFG as Low-rank Matrix Completion—0
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