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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 531–540 of 796 papers

TitleStatusHype
A Non-monotone Alternating Updating Method for A Class of Matrix Factorization Problems—0
Representation learning of drug and disease terms for drug repositioning—0
Matrix Completion via Factorizing Polynomials—0
Matrix completion with queries—0
A Riemannian gossip approach to subspace learning on Grassmann manifold—0
Targeted matrix completion—0
Matrix Completion and Related Problems via Strong Duality—0
Structured low-rank matrix learning: algorithms and applications—0
Geometric Matrix Completion with Recurrent Multi-Graph Neural NetworksCode1
Recovery of damped exponentials using structured low rank matrix completion—0
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