Robust subspace segmentation via nonconvex low rank representation

DOI10.1016/J.INS.2015.12.038zbMath1395.68233OpenAlexW2233977894MaRDI QIDQ1671711

Jing Liu, Qionghai Dai, Heng Qi, Wei Jiang

Publication date: 7 September 2018

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2015.12.038

zbMATH Keywords

nuclear norm minimizationlow rank representationsubspace segmentation\(\ell _{2,q}\)-normKy Fan \(p\)-\(k\)-norm

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (2)

Manifold adaptive kernelized low-rank representation for semisupervised image classification ⋮ Addressing label ambiguity imbalance in candidate labels: measures and disambiguation algorithm

Cites Work

• Unnamed Item
• Unnamed Item
• Nearly unbiased variable selection under minimax concave penalty
• \(k\)-plane clustering
• A cellular automaton model based on empirical observations of a driver's oscillation behavior reproducing the findings from Kerner's three-phase traffic theory
• Principal component analysis.
• Robust principal component analysis?
• Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Smoothed Low Rank and Sparse Matrix Recovery by Iteratively Reweighted Least Squares Minimization
• A Statistical View of Some Chemometrics Regression Tools
• Learning with $\ell ^{1}$-graph for image analysis

This page was built for publication: Robust subspace segmentation via nonconvex low rank representation