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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
An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion—0
Symmetric Tensor Completion from Multilinear Entries and Learning Product Mixtures over the Hypercube—0
A New Retraction for Accelerating the Riemannian Three-Factor Low-Rank Matrix Completion Algorithm—0
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion—0
Relaxed Leverage Sampling for Low-rank Matrix Completion—0
Online Matrix Completion and Online Robust PCA—0
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion—0
Low Rank Matrix Completion with Exponential Family Noise—0
Bayesian Learning for Low-Rank matrix reconstruction—0
Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion—0
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