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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 526–550 of 796 papers

TitleStatusHype
The Noisy Power Method: A Meta Algorithm with Applications—0
Nonparametric Estimation of Low Rank Matrix Valued Function—0
Nonparametric Trace Regression in High Dimensions via Sign Series Representation—0
Norm-Bounded Low-Rank Adaptation—0
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
The radius of statistical efficiency—0
Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation—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
One-sided Matrix Completion from Two Observations Per Row—0
Online Algorithms for Factorization-Based Structure from Motion—0
Online high rank matrix completion—0
The Singular Value Decomposition, Applications and Beyond—0
Online Low Rank Matrix Completion—0
Online Matrix Completion: A Collaborative Approach with Hott Items—0
Online Matrix Completion and Online Robust PCA—0
Online Matrix Completion Through Nuclear Norm Regularisation—0
Online Matrix Completion with Side Information—0
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