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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 676–700 of 796 papers

TitleStatusHype
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion—0
Matrix Completion with Noisy Entries and Outliers—0
Low Rank Matrix Completion with Exponential Family Noise—0
1-Bit Matrix Completion under Exact Low-Rank Constraint—0
Exact tensor completion using t-SVD—0
Speeding up Permutation Testing in Neuroimaging—0
Recovery of Piecewise Smooth Images from Few Fourier Samples—0
Poisson Matrix Completion—0
Noisy Tensor Completion via the Sum-of-Squares Hierarchy—0
Bayesian Learning for Low-Rank matrix reconstruction—0
Learning Parameters for Weighted Matrix Completion via Empirical Estimation—0
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly—0
Functional correspondence by matrix completion—0
Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion—0
Quantized Matrix Completion for Personalized Learning—0
Probabilistic low-rank matrix completion on finite alphabets—0
Consistent Collective Matrix Completion under Joint Low Rank Structure—0
Spectral k-Support Norm Regularization—0
Deterministic Symmetric Positive Semidefinite Matrix Completion—0
Online Optimization for Max-Norm Regularization—0
Guaranteed Matrix Completion via Non-convex Factorization—0
Signal Recovery on Graphs: Variation Minimization—0
Characterization of the equivalence of robustification and regularization in linear and matrix regression—0
PU Learning for Matrix Completion—0
Maximum Entropy Kernels for System Identification—0
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