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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 641–650 of 796 papers

TitleStatusHype
Factorizing LambdaMART for cold start recommendations—0
The Singular Value Decomposition, Applications and Beyond—0
Minimax Lower Bounds for Noisy Matrix Completion Under Sparse Factor Models—0
Boolean Matrix Factorization and Noisy Completion via Message Passing—0
High-dimensional Time Series Prediction with Missing Values—0
Exponential Family Matrix Completion under Structural Constraints—0
Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees—0
Information-theoretic Bounds on Matrix Completion under Union of Subspaces Model—0
Regret Guarantees for Item-Item Collaborative Filtering—0
Preference Completion: Large-scale Collaborative Ranking from Pairwise ComparisonsCode0
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