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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 291–300 of 796 papers

TitleStatusHype
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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