Double \(\mathrm{L}_{2, \mathrm{p}}\)-norm based PCA for feature extraction

DOI10.1016/j.ins.2021.05.079OpenAlexW3169263889MaRDI QIDQ6065873

Pu Huang, Qiaolin Ye, Fanlong Zhang, Zhangjing Yang, Wei Zhu, Guo-Wei Yang

Publication date: 11 December 2023

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

Full work available at URL: https://doi.org/10.1016/j.ins.2021.05.079

zbMATH Keywords

robustnessfeature extractionprincipal component analysisvariance maximizationreconstruction error minimization

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Learning and adaptive systems in artificial intelligence (68T05)

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Cites Work

• Unnamed Item
• 2DPCA with L1-norm for simultaneously robust and sparse modelling
• Dual robust regression for pattern classification
• Joint sparse principal component analysis
• $\ell _{2,p}$ -Norm Based PCA for Image Recognition
• Underlying Connections Between Algorithms for Nongreedy LDA-L1
• A Non-Greedy Algorithm for L1-Norm LDA
• Coherence Pursuit: Fast, Simple, and Robust Principal Component Analysis

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