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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 421–430 of 796 papers

TitleStatusHype
Matrix Completion from General Deterministic Sampling Patterns—0
Symmetric Tensor Completion from Multilinear Entries and Learning Product Mixtures over the Hypercube—0
Matrix Completion from Non-Uniformly Sampled Entries—0
Matrix Completion from O(n) Samples in Linear Time—0
Matrix Completion from Power-Law Distributed Samples—0
Matrix Completion has No Spurious Local Minimum—0
Matrix Completion in Almost-Verification Time—0
Matrix Completion-Informed Deep Unfolded Equilibrium Models for Self-Supervised k-Space Interpolation in MRI—0
Matrix Completion in Group Testing: Bounds and Simulations—0
Synthesis of Sparse Linear Arrays via Low-Rank Hankel Matrix Completion—0
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