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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 176–200 of 796 papers

TitleStatusHype
Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices—0
PAC-Bayesian Matrix Completion with a Spectral Scaled Student Prior—0
Bayesian matrix completion: prior specification—0
A new accelerated gradient method inspired by continuous-time perspective—0
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference—0
Bayesian Learning for Low-Rank matrix reconstruction—0
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion—0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Deterministic and Probabilistic Conditions for Finite Completability of Low-rank Multi-View Data—0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0—0
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
A More Stable Accelerated Gradient Method Inspired by Continuous-Time Perspective—0
Depth-Aided Color Image Inpainting in Quaternion Domain—0
Dense Air Quality Maps Using Regressive Facility Location Based Drive By Sensing—0
Demystifying Language Model Forgetting with Low-rank Example Associations—0
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework—0
A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion—0
Depth Enhancement via Low-rank Matrix Completion—0
Advancing Matrix Completion by Modeling Extra Structures beyond Low-Rankness—0
Depth Restoration: A fast low-rank matrix completion via dual-graph regularization—0
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion—0
A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms—0
Deep Non-Rigid Structure from Motion with Missing Data—0
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