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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 501–550 of 796 papers

TitleStatusHype
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsCode0
On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion—0
A New Theory for Matrix Completion—0
Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation—0
Matrix Norm Estimation from a Few EntriesCode0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution—0
Sparse Inverse Covariance Estimation for Chordal Structures—0
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural NetworksCode0
A two-dimensional decomposition approach for matrix completion through gossip—0
Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA—0
Background Subtraction via Fast Robust Matrix Completion—0
Sequential Matrix Completion—0
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization—0
SweetRS: Dataset for a recommender systems of sweetsCode0
Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective—0
Low Permutation-rank Matrices: Structural Properties and Noisy Completion—0
Robust Task Clustering for Deep Many-Task Learning—0
VIGAN: Missing View Imputation with Generative Adversarial NetworksCode0
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models—0
Nearly Optimal Robust Matrix Completion—0
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers—0
Efficient Low Rank Tensor Ring Completion—0
Recommendation via matrix completion using Kolmogorov complexity—0
Reflection Removal Using Low-Rank Matrix Completion—0
Concentration of tempered posteriors and of their variational approximations—0
Reexamining Low Rank Matrix Factorization for Trace Norm Regularization—0
Crowdsourcing with Sparsely Interacting Workers—0
Graph Convolutional Matrix CompletionCode1
Empirical Bayes Matrix Completion—0
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral FiltersCode0
A Non-monotone Alternating Updating Method for A Class of Matrix Factorization Problems—0
Representation learning of drug and disease terms for drug repositioning—0
Matrix Completion via Factorizing Polynomials—0
Matrix completion with queries—0
A Riemannian gossip approach to subspace learning on Grassmann manifold—0
Targeted matrix completion—0
Matrix Completion and Related Problems via Strong Duality—0
Structured low-rank matrix learning: algorithms and applications—0
Geometric Matrix Completion with Recurrent Multi-Graph Neural NetworksCode1
Recovery of damped exponentials using structured low rank matrix completion—0
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis—0
Fundamental Conditions for Low-CP-Rank Tensor Completion—0
Novel Structured Low-rank algorithm to recover spatially smooth exponential image time series—0
Algebraic Variety Models for High-Rank Matrix CompletionCode0
Spectrum Estimation from a Few Entries—0
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis—0
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM—0
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems—0
Exact tensor completion with sum-of-squares—0
SAR: Semantic Analysis for Recommendation—0
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