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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 751–760 of 796 papers

TitleStatusHype
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Bayesian Learning for Low-Rank matrix reconstruction—0
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference—0
Bayesian matrix completion: prior specification—0
Social Trust Prediction via Max-norm Constrained 1-bit Matrix Completion—0
PAC-Bayesian Matrix Completion with a Spectral Scaled Student Prior—0
Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices—0
Binary Matrix Completion Using Unobserved Entries—0
Binary matrix completion with nonconvex regularizers—0
Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering—0
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