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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
Ranking with Features: Algorithm and A Graph Theoretic Analysis—0
Improving Temporal Interpolation of Head and Body Pose using Gaussian Process Regression in a Matrix Completion Setting—0
Mixture Matrix Completion—0
Fusion Subspace Clustering: Full and Incomplete Data—0
Matrix completion and extrapolation via kernel regression—0
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
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion—0
A Unified Framework for Structured Low-rank Matrix Learning—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
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