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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
Leveraged Matrix Completion with Noise—0
Matrix Completion with Noisy Entries and Outliers—0
Matrix Completion with Noisy Side Information—0
Matrix Completion with Nonconvex Regularization: Spectral Operators and Scalable Algorithms—0
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM—0
Matrix completion with queries—0
Matrix Completion With Selective Sampling—0
Matrix Completion with Sparse Noisy Rows—0
Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference—0
Matrix Completion with Weighted Constraint for Haplotype Estimation—0
Matrix Data Deep Decoder - Geometric Learning for Structured Data Completion—0
Matrix Decomposition on Graphs: A Functional View—0
Matrix Estimation for Offline Reinforcement Learning with Low-Rank Structure—0
Matrix Factorization via Deep Learning—0
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms—0
Targeted matrix completion—0
Spectral Perturbation Meets Incomplete Multi-view Data—0
Temporal Matrix Completion with Locally Linear Latent Factors for Medical Applications—0
Maximum Entropy Kernels for System Identification—0
Max-Norm Optimization for Robust Matrix Recovery—0
MC2G: An Efficient Algorithm for Matrix Completion with Social and Item Similarity Graphs—0
Median Matrix Completion: from Embarrassment to Optimality—0
Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA—0
Tensor Completion by Alternating Minimization under the Tensor Train (TT) Model—0
Tensor Completion Made Practical—0
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