A novel feature learning framework for high-dimensional data classification-Reference-Cited by-同舟云学术

A novel feature learning framework for high-dimensional data classification

Published:2020-11-02 Issue:2 Volume:12 Page:555-569
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Li Yanxia,Chai Yi,Yin Hongpeng^ORCID,Chen Bo

Funder

Key Programme

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/content/pdf/10.1007/s13042-020-01188-2.pdf

Reference64 articles.

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2. Sun W, Xie S, Han N (2019) Robust discriminant analysis with adaptive locality preserving. Int J Mach Learn Cybern 10:2791–2804

3. Wu Y, Hoi SCH, Mei T, Yu N (2017) Large-scale online feature selection for ultra-high dimensional sparse data. ACM Trans Knowl Discov Data 11(4):48.1–48.22

4. Tan M, Tsang IW, Wang L (2013) Minimax sparse logistic regression for very high-dimensional feature selection. IEEE Trans Neural Netw Learn Syst 24(10):1609–1622

5. Tan M, Wang L, Tsang IW (2010) Learning sparse SVM for feature selection on very high dimensional datasets. Proc Int Conf Mach Learn 2010:1047–1054

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