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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 451–475 of 796 papers

TitleStatusHype
Collective Matrix CompletionCode0
Unsupervised Metric Learning in Presence of Missing DataCode0
A Unified Framework for Sparse Relaxed Regularized Regression: SR3—0
Scalable Recommender Systems through Recursive Evidence Chains—0
Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning—0
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery—0
A Unified Framework for Structured Low-rank Matrix Learning—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion—0
Matrix Completion from Non-Uniformly Sampled Entries—0
Ranking Recovery from Limited Comparisons using Low-Rank Matrix Completion—0
Matrix Completion and Performance Guarantees for Single Individual Haplotyping—0
Causal Inference with Noisy and Missing Covariates via Matrix FactorizationCode0
k-Space Deep Learning for Reference-free EPI Ghost Correction—0
Nonlinear Inductive Matrix Completion based on One-layer Neural Networks—0
Scalable and Robust Community Detection with Randomized Sketching—0
Matrix Co-completion for Multi-label Classification with Missing Features and Labels—0
Communication-Efficient Projection-Free Algorithm for Distributed Optimization—0
Transduction with Matrix Completion Using Smoothed Rank Function—0
Projection-Free Bandit Convex Optimization—0
Extendable Neural Matrix Completion—0
k-Space Deep Learning for Accelerated MRICode1
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
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