2DPCA with L1-norm for simultaneously robust and sparse modelling

DOI10.1016/j.neunet.2013.06.002zbMath1296.68150OpenAlexW1979714579WikidataQ44704391 ScholiaQ44704391MaRDI QIDQ461155

Haixian Wang, Jing Wang

Publication date: 10 October 2014

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2013.06.002

zbMATH Keywords

dimensionality reduction2DPCA-L1lasso regularizationrobust modelling

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Image analysis in multivariate analysis (62H35) Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies for image processing (68U10)

Related Items (7)

Principal component analysis based on nuclear norm minimization ⋮ L1-norm and maximum margin criterion based discriminant locality preserving projections via trace Lasso ⋮ L1-norm-based principal component analysis with adaptive regularization ⋮ Double \(\mathrm{L}_{2, \mathrm{p}}\)-norm based PCA for feature extraction ⋮ A joint-norm distance metric 2DPCA for robust dimensionality reduction ⋮ Robust multi-view discriminant analysis with view-consistency ⋮ F-norm distance metric based robust 2DPCA and face recognition

Uses Software

• PDCO

Cites Work

• Unnamed Item
• Manifold elastic net: a unified framework for sparse dimension reduction
• Improve robustness of sparse PCA by \(L_{1}\)-norm maximization
• Principal component analysis.
• Atomic Decomposition by Basis Pursuit

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