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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 581–590 of 796 papers

TitleStatusHype
Max-Norm Optimization for Robust Matrix Recovery—0
Tensor Completion by Alternating Minimization under the Tensor Train (TT) Model—0
Noisy Inductive Matrix Completion Under Sparse Factor Models—0
Robust Spectral Detection of Global Structures in the Data by Learning a Regularization—0
Tracking Completion—0
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing—0
Which Tumblr Post Should I Read Next?—0
Learning of Generalized Low-Rank Models: A Greedy Approach—0
Fast Methods for Recovering Sparse Parameters in Linear Low Rank Models—0
Hybrid Recommender System based on AutoencodersCode0
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