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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 401–410 of 796 papers

TitleStatusHype
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence—0
Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation—0
On Low-rank Trace Regression under General Sampling DistributionCode0
Matrix Completion With Selective Sampling—0
Binary matrix completion with nonconvex regularizers—0
Multi-View Matrix Completion for Multi-Label Image Classification—0
Cluster Developing 1-Bit Matrix Completion—0
Ensemble Methods for Causal Effects in Panel Data Settings—0
Machine Learning Methods Economists Should Know About—0
State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual PredictionCode0
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