Linear embedding by joint robust discriminant analysis and inter-class sparsity

DOI10.1016/J.NEUNET.2020.04.018zbMath1472.62093DBLPjournals/nn/DornaikaK20OpenAlexW3019871518WikidataQ94495695 ScholiaQ94495695MaRDI QIDQ1982415

A. Khoder, F. Dornaika

Publication date: 8 September 2021

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2020.04.018

zbMATH Keywords

feature extractionimage classificationfeature selectionlinear discriminant analysisinter-class sparsity

Mathematics Subject Classification ID

Nonparametric robustness (62G35) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05)

Related Items (2)

An enhanced approach to the robust discriminant analysis and class sparsity based embedding ⋮ Feature extraction framework based on contrastive learning with adaptive positive and negative samples

Uses Software

• LIBSVM
• LMaFit
• ImageNet
• t-SNE
• Caltech-256
• COIL-20
• LFW
• AlexNet

Cites Work

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• Low-rank and sparse embedding for dimensionality reduction
• Learning a discriminant graph-based embedding with feature selection for image categorization
• Classifier technology and the illusion of progress
• Inductive and flexible feature extraction for semi-supervised pattern categorization
• Robust principal component analysis?
• Regularization and Variable Selection Via the Elastic Net
• Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization

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