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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 751–796 of 796 papers

TitleStatusHype
Practical Matrix Completion and Corruption Recovery using Proximal Alternating Robust Subspace Minimization—0
Manopt, a Matlab toolbox for optimization on manifolds—0
Flexible Low-Rank Statistical Modeling with Side Information—0
On the Predictability of Human Assessment: when Matrix Completion Meets NLP Evaluation—0
Completing Any Low-rank Matrix, Provably—0
R3MC: A Riemannian three-factor algorithm for low-rank matrix completion—0
Provable Inductive Matrix Completion—0
Robust Spectral Compressed Sensing via Structured Matrix Completion—0
Low-rank optimization for distance matrix completion—0
Spectral Compressed Sensing via Structured Matrix Completion—0
Matrix Completion via Max-Norm Constrained Optimization—0
Missing Entries Matrix Approximation and Completion—0
Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion—0
Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation—0
Coherence and sufficient sampling densities for reconstruction in compressed sensing—0
Scaled Gradients on Grassmann Manifolds for Matrix Completion—0
Approximating Concavely Parameterized Optimization Problems—0
Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning—0
Calibrated Elastic Regularization in Matrix Completion—0
Relax and Randomize : From Value to Algorithms—0
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion—0
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
Fixed-rank matrix factorizations and Riemannian low-rank optimization—0
Low-rank optimization with trace norm penalty—0
RTRMC: A Riemannian trust-region method for low-rank matrix completion—0
SpaRCS: Recovering low-rank and sparse matrices from compressive measurements—0
A Denoising View of Matrix Completion—0
Penalty Decomposition Methods for Rank Minimization—0
Online Robust Subspace Tracking from Partial InformationCode0
Distributed Matrix Completion and Robust Factorization—0
Sparse Bayesian Methods for Low-Rank Matrix Estimation—0
Matrix completion with column manipulation: Near-optimal sample-robustness-rank tradeoffs—0
A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms—0
Large-Scale Matrix Factorization with Missing Data under Additional Constraints—0
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm—0
Transduction with Matrix Completion: Three Birds with One Stone—0
Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development—0
Link Discovery using Graph Feature Tracking—0
Nuclear norm penalization and optimal rates for noisy low rank matrix completion—0
Robust PCA via Outlier PursuitCode0
Online Identification and Tracking of Subspaces from Highly Incomplete InformationCode0
Matrix Completion from Power-Law Distributed Samples—0
A Gradient Descent Algorithm on the Grassman Manifold for Matrix CompletionCode0
Guaranteed Rank Minimization via Singular Value ProjectionCode0
Matrix Completion from Noisy EntriesCode0
Matrix Completion from a Few EntriesCode0
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