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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 51–75 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
Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size—0
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models—0
Fixed-rank matrix factorizations and Riemannian low-rank optimization—0
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion—0
Adaptive Noisy Matrix Completion—0
Depth Enhancement via Low-rank Matrix Completion—0
Deep learned SVT: Unrolling singular value thresholding to obtain better MSE—0
An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion—0
High-Rank Matrix Completion and Clustering under Self-Expressive Models—0
High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization—0
Harmonic Retrieval Using Weighted Lifted-Structure Low-Rank Matrix Completion—0
Decentralized Singular Value Decomposition for Large-scale Distributed Sensor Networks—0
Low-rank matrix completion theory via Plucker coordinates—0
Advancing Matrix Completion by Modeling Extra Structures beyond Low-Rankness—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution—0
Data-based system representations from irregularly measured data—0
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