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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 571–580 of 796 papers

TitleStatusHype
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis—0
Notes on Low-rank Matrix Factorization—0
Novel Structured Low-rank algorithm to recover spatially smooth exponential image time series—0
Nuclear norm penalization and optimal rates for noisy low rank matrix completion—0
Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion—0
Ocean Reverberation Suppression via Matrix Completion with Sensor Failure—0
On adaptivity and minimax optimality of two-sided nearest neighbors—0
On Asymptotic Linear Convergence of Projected Gradient Descent for Constrained Least Squares—0
On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion—0
One-Bit Matrix Completion with Differential Privacy—0
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