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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
Ranking with Features: Algorithm and A Graph Theoretic Analysis—0
Improving Temporal Interpolation of Head and Body Pose using Gaussian Process Regression in a Matrix Completion Setting—0
Mixture Matrix Completion—0
Fusion Subspace Clustering: Full and Incomplete Data—0
Matrix completion and extrapolation via kernel regression—0
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
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