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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 101–125 of 796 papers

TitleStatusHype
Matrix Completion from Noisy EntriesCode0
Matrix Completion in the Unit Hypercube via Structured Matrix FactorizationCode0
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale DatasetCode0
Bayesian Matrix Completion via Adaptive Relaxed Spectral RegularizationCode0
A Perturbation Bound on the Subspace Estimator from Canonical ProjectionsCode0
Matrix Completion with Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR SamplingCode0
Adversarially-Trained Nonnegative Matrix FactorizationCode0
Matrix Low-Rank Trust Region Policy OptimizationCode0
Matrix tri-factorization over the tropical semiringCode0
Applications of Nature-Inspired Metaheuristic Algorithms for Tackling Optimization Problems Across DisciplinesCode0
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsCode0
NGS Based Haplotype Assembly Using Matrix CompletionCode0
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine LearningCode0
Bounded Simplex-Structured Matrix Factorization: Algorithms, Identifiability and ApplicationsCode0
Adaptive Matrix Completion for the Users and the Items in TailCode0
Online Identification and Tracking of Subspaces from Highly Incomplete InformationCode0
Can We Predict Performance of Large Models across Vision-Language Tasks?Code0
An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU functionCode0
An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex OptimizationCode0
Partial Trace Regression and Low-Rank Kraus DecompositionCode0
Causal Inference with Noisy and Missing Covariates via Matrix FactorizationCode0
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust RecommendationCode0
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral FiltersCode0
Preference Completion: Large-scale Collaborative Ranking from Pairwise ComparisonsCode0
Deep Collective Matrix Factorization for Augmented Multi-View LearningCode0
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