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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 101–110 of 796 papers

TitleStatusHype
Bayesian Learning for Low-Rank matrix reconstruction—0
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference—0
Bounded Manifold Completion—0
A Riemannian gossip approach to decentralized 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
Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction—0
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