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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 351–400 of 796 papers

TitleStatusHype
Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks—0
Bounded Manifold Completion—0
Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning—0
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework—0
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
Community Detection and Matrix Completion with Social and Item Similarity Graphs—0
Influenza Modeling Based on Massive Feature Engineering and International Flow Deconvolution—0
A Fast Matrix-Completion-Based Approach for Recommendation Systems—0
Provable Non-linear Inductive Matrix Completion—0
Matrix Completion using Kronecker Product Approximation—0
Spectral Geometric Matrix CompletionCode0
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery—0
Intelligent Reflecting Surface for Massive Device Connectivity: Joint Activity Detection and Channel Estimation—0
Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm AssumptionCode0
Structured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine LearningCode0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion—0
Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift—0
Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data—0
New and Explicit Constructions of Unbalanced Ramanujan Bipartite Graphs—0
The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion—0
Deep geometric matrix completion: Are we doing it right?—0
Statistical Inferences of Linear Forms for Noisy Matrix Completion—0
Scalable Probabilistic Matrix Factorization with Graph-Based PriorsCode0
Deterministic Completion of Rectangular Matrices Using Asymmetric Ramanujan Graphs: Exact and Stable Recovery—0
Scalable Bayesian Non-linear Matrix Completion—0
Deep Non-Rigid Structure from Motion with Missing Data—0
Collaborative Filtering and Multi-Label Classification with Matrix Factorization—0
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk—0
Low-rank matrix completion and denoising under Poisson noise—0
A divide-and-conquer algorithm for binary matrix completion—0
Depth Restoration: A fast low-rank matrix completion via dual-graph regularization—0
Approximate matrix completion based on cavity method—0
Online Variational Bayesian Subspace Filtering with Applications—0
Generalization error bounds for kernel matrix completion and extrapolation—0
Online Matrix Completion with Side Information—0
Efficiently escaping saddle points on manifolds—0
Inference and Uncertainty Quantification for Noisy Matrix Completion—0
Graphon Estimation from Partially Observed Network DataCode0
Guaranteed Matrix Completion Under Multiple Linear Transformations—0
Implicit Regularization in Deep Matrix FactorizationCode0
Spectral Perturbation Meets Incomplete Multi-view Data—0
Sum-of-squares meets square loss: Fast rates for agnostic tensor completion—0
Matrix Completion in the Unit Hypercube via Structured Matrix FactorizationCode0
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender SystemsCode0
Collaborative Self-Attention for Recommender Systems—0
Prediction with Unpredictable Feature Evolution—0
Inductive Matrix Completion Based on Graph Neural NetworksCode1
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers—0
Adaptive Matrix Completion for the Users and the Items in TailCode0
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