Relations Among Some Low-Rank Subspace Recovery Models

DOI10.1162/NECO_a_00762zbMath1418.62250arXiv1412.2196WikidataQ40751270 ScholiaQ40751270MaRDI QIDQ5380321

Chao Zhang, Hongyang Zhang, Junbin Gao, Zhouchen Lin

Publication date: 4 June 2019

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1412.2196

zbMATH Keywords

robust low-rank representation (R-LRR)robust principal component analysis (R-PCA)

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Nonparametric robustness (62G35)

Related Items (4)

A nonconvex formulation for low rank subspace clustering: algorithms and convergence analysis ⋮ Unnamed Item ⋮ Fast Estimation of Approximate Matrix Ranks Using Spectral Densities ⋮ Decomposition into low-rank plus additive matrices for background/foreground separation: a review for a comparative evaluation with a large-scale dataset

Uses Software

• Blendenpik
• RASL

Cites Work

• Unnamed Item
• A framework for robust subspace learning
• TILT: transform invariant low-rank textures
• A geometric analysis of subspace clustering with outliers
• Two proposals for robust PCA using semidefinite programming
• Robust computation of linear models by convex relaxation
• Identification of hybrid systems. A tutorial
• Subspace system identification for training-based MIMO channel estimation
• A Novel M-Estimator for Robust PCA
• Robust principal component analysis?
• Rank-Sparsity Incoherence for Matrix Decomposition
• Randomized Algorithms for Matrices and Data
• Blendenpik: Supercharging LAPACK's Least-Squares Solver
• Robust PCA via Outlier Pursuit
• Robust Matrix Decomposition With Sparse Corruptions

This page was built for publication: Relations Among Some Low-Rank Subspace Recovery Models