Sparse feature selection using hypergraph Laplacian-based semi-supervised discriminant analysis-Reference-Cited by-同舟云学术

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Sparse feature selection using hypergraph Laplacian-based semi-supervised discriminant analysis

Published:2025-01 Issue: Volume:157 Page:110882
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Sheikhpour Razieh,Berahmand Kamal^ORCID,Mohammadi Mehrnoush,Khosravi Hassan

Publisher

Elsevier BV

Reference37 articles.

1. Unsupervised feature selection guided by orthogonal representation of feature space;Jahani;Neurocomputing,2023

2. Efficient local coherent structure learning via self-evolution bipartite graph;Wang;IEEE Trans. Cybern.,2024

3. Worst-case discriminative feature learning via max-min ratio analysis;Wang;IEEE Trans. Pattern Anal. Mach. Intell.,2024

4. Efficient hybrid nature-inspired binary optimizers for feature selection;Mafarja;Cogn. Comput.,2020

5. Low-redundant unsupervised feature selection based on data structure learning and feature orthogonalization;Samareh-Jahani;Expert Syst. Appl.,2024

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