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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 11–20 of 796 papers

TitleStatusHype
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order MethodCode1
Generalized Low Rank ModelsCode1
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few SamplesCode1
GLocal-K: Global and Local Kernels for Recommender SystemsCode1
Crosslingual Topic Modeling with WikiPDACode1
Adversarial Crowdsourcing Through Robust Rank-One Matrix CompletionCode1
Causal Matrix CompletionCode1
Compressed sensing of low-rank plus sparse matricesCode1
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient DescentCode1
Indiscriminate Poisoning Attacks on Unsupervised Contrastive LearningCode1
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