Global structure-guided neighborhood preserving embedding for dimensionality reduction-Reference-Cited by-同舟云学术

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Global structure-guided neighborhood preserving embedding for dimensionality reduction

Published:2022-01-11 Issue:7 Volume:13 Page:2013-2032
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Gao Can,Li Yong,Zhou Jie^ORCID,Pedrycz Witold,Lai Zhihui,Wan Jun,Lu Jianglin

Funder

National Natural Science Foundation of China

natural science foundation of guangdong province

Shenzhen Science and Technology Program

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-021-01502-6.pdf

Reference52 articles.

1. Li Y, Chai Y, Yin H et al (2020) A novel feature learning framework for high-dimensional data classification. Int J Mach Learn Cybern. https://doi.org/10.1007/s13042-020-01188-2

2. Hu Q, Zhang L, Zhou Y et al (2018) Large-scale multimodality attribute reduction with multi-kernel fuzzy rough sets. IEEE Trans Fuzzy Syst 26(1):226–238

3. Li J, Mei C, Xu W et al (2015) Concept learning via granular computing: a cognitive viewpoint. Inf Sci 298:447–467

4. Qian J, Yang J, Xu Y et al (2020) Image decomposition based matrix regression with applications to robust face recognition. Pattern Recognit 102:107204

5. Shang R, Chang J, Jiao L et al (2019) Unsupervised feature selection based on self-representation sparse regression and local similarity preserving. Int J Mach Learn Cybern 10:757–770

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