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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 781–790 of 796 papers

TitleStatusHype
Sparse Bayesian Methods for Low-Rank Matrix Estimation—0
Matrix completion with column manipulation: Near-optimal sample-robustness-rank tradeoffs—0
A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms—0
Large-Scale Matrix Factorization with Missing Data under Additional Constraints—0
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm—0
Transduction with Matrix Completion: Three Birds with One Stone—0
Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development—0
Link Discovery using Graph Feature Tracking—0
Nuclear norm penalization and optimal rates for noisy low rank matrix completion—0
Robust PCA via Outlier PursuitCode0
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