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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 61–70 of 796 papers

TitleStatusHype
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing—0
A Non-monotone Alternating Updating Method for A Class of Matrix Factorization Problems—0
A Riemannian gossip approach to decentralized matrix completion—0
A Sparse Interactive Model for Matrix Completion with Side Information—0
Convergence of the majorized PAM method with subspace correction for low-rank composite factorization model—0
A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion—0
Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion—0
A majorization-minimization algorithm for nonnegative binary matrix factorization—0
Always Valid Risk Monitoring for Online Matrix Completion—0
A divide-and-conquer algorithm for binary matrix completion—0
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