Unsupervised feature selection based on minimum-redundant subspace learning with self-weighted adaptive graph-Reference-Cited by-同舟云学术

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Unsupervised feature selection based on minimum-redundant subspace learning with self-weighted adaptive graph

Published:2024-12 Issue: Volume:155 Page:104738
ISSN:1051-2004
Container-title:Digital Signal Processing
language:en
Short-container-title:Digital Signal Processing

Author:

Ma Ziping^ORCID,Wei Yilong,Huang Yulei,Wang Jingyu

Funder

Central Public Research Institutes Basic Funds for Research and Development

School of Mathematics, Institute for Research in Fundamental Sciences

National Aerospace Science Foundation of China

Graduate Innovation Project of North Minzu University

Guangxi Higher Institutions Scientific Research Project

North Minzu University

Publisher

Elsevier BV

Reference41 articles.

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

2. Sparse trace ratio LDA for supervised feature selection;Li;IEEE Trans. Cybern.,2023

3. Feature selection with maximal relevance and minimal supervised redundancy;Wang;IEEE Trans. Cybern.,2023

4. Robust spectral learning for unsupervised feature selection;Shi,2014

5. L. Chen, Z. Zhong, Adaptive and structured graph learning for semi-supervised clustering, Inf. Process. Manage, 2022;59(4): 0306-4573, https://doi.org/10.1016/j.ipm.2022.102949.

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