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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 121–130 of 796 papers

TitleStatusHype
Annotation Projection-based Representation Learning for Cross-lingual Dependency Parsing—0
Adaptive Noisy Matrix Completion—0
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing—0
CAYLEYNETS: SPECTRAL GRAPH CNNS WITH COMPLEX RATIONAL FILTERS—0
Characterization of the equivalence of robustification and regularization in linear and matrix regression—0
Clipped Matrix Completion: A Remedy for Ceiling Effects—0
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
A Proximal Modified Quasi-Newton Method for Nonsmooth Regularized Optimization—0
A Latent Feature Analysis-based Approach for Spatio-Temporal Traffic Data Recovery—0
Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation—0
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