Unsupervised feature selection based on bipartite graph and low-redundant regularization-Reference-Cited by-同舟云学术

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Unsupervised feature selection based on bipartite graph and low-redundant regularization

Published:2024-10 Issue: Volume:302 Page:112379
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Xiang Longyan,Chen Hongmei^ORCID,Yin Tengyu,Horng Shi-Jinn^ORCID,Li Tianrui^ORCID

Publisher

Elsevier BV

Reference47 articles.

1. Combining a multi-feature neural network with multi-task learning for emergency calls severity prediction;Abi Kanaan;Array,2024

2. Multi-modal self-paced locality preserving learning for diagnosis of Alzheimer’s disease;Hao;IEEE Trans. Cogn. Dev. Syst.,2023

3. FG-HFS: A feature filter and group evolution hybrid feature selection algorithm for high-dimensional gene expression data;Xu;Expert Syst. Appl.,2024

4. Action-stage emphasized spatiotemporal VLAD for video action recognition;Tu;IEEE Trans. Image Process.,2019

5. An encrypted traffic identification method based on multi-scale feature fusion;Zhu;Array,2024

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