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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 351–375 of 796 papers

TitleStatusHype
Influenza Modeling Based on Massive Feature Engineering and International Flow Deconvolution—0
Information-theoretic Bounds on Matrix Completion under Union of Subspaces Model—0
Intelligent Reflecting Surface for Massive Device Connectivity: Joint Activity Detection and Channel Estimation—0
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers—0
Going off the Grid: Iterative Model Selection for Biclustered Matrix Completion—0
Introducing the Huber mechanism for differentially private low-rank matrix completion—0
Computational Limits for Matrix Completion—0
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System—0
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
Computational Graph Completion—0
L_2,1-Norm Regularized Quaternion Matrix Completion Using Sparse Representation and Quaternion QR Decomposition—0
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers—0
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems—0
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion—0
Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development—0
Learning Counterfactual Distributions via Kernel Nearest Neighbors—0
Learning from Ambiguously Labeled Face Images—0
Learning Iterative Reasoning through Energy Diffusion—0
Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion—0
Learning Mixtures of Discrete Product Distributions using Spectral Decompositions—0
Learning of Generalized Low-Rank Models: A Greedy Approach—0
Learning Parameters for Weighted Matrix Completion via Empirical Estimation—0
Learning Transition Operators From Sparse Space-Time Samples—0
Learning Translations via Matrix Completion—0
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization—0
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