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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 476–500 of 796 papers

TitleStatusHype
Sparse Group Inductive Matrix Completion—0
Tensor Methods for Nonlinear Matrix Completion—0
Parametric Models for Mutual Kernel Matrix Completion—0
Exact Reconstruction of Euclidean Distance Geometry Problem Using Low-rank Matrix Completion—0
Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion—0
Non-Convex Matrix Completion Against a Semi-Random Adversary—0
Tensor graph convolutional neural network—0
Leave-one-out Approach for Matrix Completion: Primal and Dual Analysis—0
Approximate Method of Variational Bayesian Matrix Factorization/Completion with Sparse Prior—0
Binary Matrix Completion Using Unobserved Entries—0
Deep Models of Interactions Across SetsCode0
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes FlowCode0
Convolutional Geometric Matrix Completion—0
Matrices with Gaussian noise: optimal estimates for singular subspace perturbation—0
Static and Dynamic Robust PCA and Matrix Completion: A Review—0
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form—0
Sequence-Aware Recommender SystemsCode0
Structured low-rank matrix completion for forecasting in time series analysis—0
Nonparametric Estimation of Low Rank Matrix Valued Function—0
Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion—0
Active Feature Acquisition with Supervised Matrix Completion—0
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion—0
Matrix completion with deterministic pattern - a geometric perspective—0
NGS Based Haplotype Assembly Using Matrix CompletionCode0
Matrix Completion for Structured Observations—0
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