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Matrix Completion

Matrix Completion is a method for recovering lost information. It originates from machine learning and usually deals with highly sparse matrices. Missing or unknown data is estimated using the low-rank matrix of the known data.

Source: A Fast Matrix-Completion-Based Approach for Recommendation Systems

Papers

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Showing 276–300 of 796 papers

TitleStatusHype
Flat minima generalize for low-rank matrix recovery—0
Flexible Low-Rank Statistical Modeling with Side Information—0
Column _2,0-norm regularized factorization model of low-rank matrix recovery and its computation—0
Forecasting Nonnegative Time Series via Sliding Mask Method (SMM) and Latent Clustered Forecast (LCF)—0
Functional correspondence by matrix completion—0
Fundamental Conditions for Low-CP-Rank Tensor Completion—0
Fusion Subspace Clustering: Full and Incomplete Data—0
Generalization Bounds for Inductive Matrix Completion in Low-noise Settings—0
Generalization error bounds for kernel matrix completion and extrapolation—0
Generalized Conditional Gradient for Sparse Estimation—0
Generalized Low-Rank Matrix Completion Model with Overlapping Group Error Representation—0
Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion—0
Completing Any Low-rank Matrix, Provably—0
Geometric Inference for General High-Dimensional Linear Inverse Problems—0
Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis—0
Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion—0
Geometric Matrix Completion with Deep Conditional Random Fields—0
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers—0
Coherence and sufficient sampling densities for reconstruction in compressed sensing—0
Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size—0
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems—0
A note on the statistical view of matrix completion—0
Computational Graph Completion—0
Going off the Grid: Iterative Model Selection for Biclustered Matrix Completion—0
Graph-Based Matrix Completion Applied to Weather Data—0
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