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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 751–775 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
BlockEcho: Retaining Long-Range Dependencies for Imputing Block-Wise Missing Data—0
Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints—0
Boolean Matrix Factorization and Noisy Completion via Message Passing—0
Bounded Manifold Completion—0
Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks—0
Spectal Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning—0
Calibrated Elastic Regularization in Matrix Completion—0
Fixing Inventory Inaccuracies At Scale—0
Categorical Matrix Completion—0
Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models—0
Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy—0
Solving the Robust Matrix Completion Problem via a System of Nonlinear Equations—0
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems—0
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability—0
CAYLEYNETS: SPECTRAL GRAPH CNNS WITH COMPLEX RATIONAL FILTERS—0
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