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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 631–640 of 796 papers

TitleStatusHype
Probabilistic low-rank matrix completion on finite alphabets—0
Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms—0
Prognostics of Surgical Site Infections using Dynamic Health Data—0
Progresses and Challenges in Link Prediction—0
Projected Wirtinger Gradient Descent for Low-Rank Hankel Matrix Completion in Spectral Compressed Sensing—0
Projection-Free Algorithm for Stochastic Bi-level Optimization—0
Projection-Free Bandit Convex Optimization—0
Propagation Map Reconstruction via Interpolation Assisted Matrix Completion—0
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent—0
Provable Inductive Matrix Completion—0
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