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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 51–60 of 158 papers

TitleStatusHype
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
Depth Restoration: A fast low-rank matrix completion via dual-graph regularization—0
Entry-Specific Bounds for Low-Rank Matrix Completion under Highly Non-Uniform Sampling—0
Errata: Distant Supervision for Relation Extraction with Matrix Completion—0
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion—0
A Pre-training Oracle for Predicting Distances in Social Networks—0
Exact Linear Convergence Rate Analysis for Low-Rank Symmetric Matrix Completion via Gradient Descent—0
Exact Reconstruction of Euclidean Distance Geometry Problem Using Low-rank Matrix Completion—0
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery—0
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion—0
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