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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 541–550 of 796 papers

TitleStatusHype
Structured low-rank matrix learning: algorithms and applications—0
Recovery of damped exponentials using structured low rank matrix completion—0
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis—0
Fundamental Conditions for Low-CP-Rank Tensor Completion—0
Novel Structured Low-rank algorithm to recover spatially smooth exponential image time series—0
Algebraic Variety Models for High-Rank Matrix CompletionCode0
Spectrum Estimation from a Few Entries—0
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis—0
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM—0
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems—0
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