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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 191–200 of 796 papers

TitleStatusHype
A majorization-minimization algorithm for nonnegative binary matrix factorization—0
Quaternion Optimized Model with Sparse Regularization for Color Image Recovery—0
MultiEarth 2022 -- Multimodal Learning for Earth and Environment Workshop and Challenge—0
Survey of Matrix Completion Algorithms—0
Matrix Completion with Sparse Noisy Rows—0
Matrix Completion with Heterogonous Cost—0
Sensing Theorems for Unsupervised Learning in Linear Inverse ProblemsCode1
Perturbation Analysis of Randomized SVD and its Applications to Statistics—0
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference—0
Hierarchical Clustering and Matrix Completion for the Reconstruction of World Input-Output Tables—0
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