SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 111–120 of 796 papers

TitleStatusHype
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsCode0
Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm AssumptionCode0
Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete DataCode0
Bounded Simplex-Structured Matrix Factorization: Algorithms, Identifiability and ApplicationsCode0
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine LearningCode0
Nonnegative Tensor Completion via Integer OptimizationCode0
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust RecommendationCode0
An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU functionCode0
An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex OptimizationCode0
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral FiltersCode0
Show:102550
← PrevPage 12 of 80Next →

No leaderboard results yet.