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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 226–250 of 796 papers

TitleStatusHype
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference—0
Hierarchical Clustering and Matrix Completion for the Reconstruction of World Input-Output Tables—0
Adaptive Noisy Matrix Completion—0
Lifelong Matrix Completion with Sparsity-Number—0
Flat minima generalize for low-rank matrix recovery—0
Matrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning—0
High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization—0
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex MinimizationCode0
Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects—0
Two-snapshot DOA Estimation via Hankel-structured Matrix Completion—0
Splitting numerical integration for matrix completion—0
Counterfactual inference for sequential experiments—0
Color Image Inpainting via Robust Pure Quaternion Matrix Completion: Error Bound and Weighted Loss—0
Inductive Matrix Completion: No Bad Local Minima and a Fast AlgorithmCode0
LRSVRG-IMC: An SVRG-Based Algorithm for LowRank Inductive Matrix Completion—0
Dense Air Quality Maps Using Regressive Facility Location Based Drive By Sensing—0
Variational Bayesian Filtering with Subspace Information for Extreme Spatio-Temporal Matrix Completion—0
Matrix Completion with Hierarchical Graph Side Information—0
On Asymptotic Linear Convergence of Projected Gradient Descent for Constrained Least Squares—0
A More Stable Accelerated Gradient Method Inspired by Continuous-Time Perspective—0
Fine-grained Generalization Analysis of Inductive Matrix Completion—0
PAC-Bayesian matrix completion with a spectral scaled Student prior—0
Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction—0
Nonnegative Tensor Completion via Integer OptimizationCode0
Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers—0
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