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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 451–460 of 796 papers

TitleStatusHype
Collective Matrix CompletionCode0
Unsupervised Metric Learning in Presence of Missing DataCode0
A Unified Framework for Sparse Relaxed Regularized Regression: SR3—0
Scalable Recommender Systems through Recursive Evidence Chains—0
Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning—0
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery—0
A Unified Framework for Structured Low-rank Matrix Learning—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion—0
Matrix Completion from Non-Uniformly Sampled Entries—0
Ranking Recovery from Limited Comparisons using Low-Rank Matrix Completion—0
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