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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 71–80 of 796 papers

TitleStatusHype
A divide-and-conquer algorithm for binary matrix completion—0
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM—0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization—0
Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees—0
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems—0
A Riemannian gossip approach to subspace learning on Grassmann manifold—0
A Denoising View of Matrix Completion—0
1-Bit Matrix Completion under Exact Low-Rank Constraint—0
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features—0
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