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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 401–410 of 796 papers

TitleStatusHype
High-dimensional Time Series Prediction with Missing Values—0
High-Rank Matrix Completion and Clustering under Self-Expressive Models—0
Identifying global optimality for dictionary learning—0
Identifying Influential Entries in a Matrix—0
IHT-Inspired Neural Network for Single-Snapshot DOA Estimation with Sparse Linear Arrays—0
Image Tag Completion and Refinement by Subspace Clustering and Matrix Completion—0
Implicit bias of SGD in L_2-regularized linear DNNs: One-way jumps from high to low rank—0
Implicit Regularization in Deep Tensor Factorization—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion—0
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