Sparse Trace Ratio LDA for Supervised Feature Selection-Reference-Cited by-同舟云学术

Sparse Trace Ratio LDA for Supervised Feature Selection

Published:2023 Issue: Volume: Page:1-14
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Li Zhengxin¹^ORCID,Nie Feiping²^ORCID,Wu Danyang³,Wang Zheng²^ORCID,Li Xuelong²^ORCID

Affiliation:

1. Equipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi’an, China

2. School of Computer Science and the School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University, Xi’an, China

3. State Key Laboratory for Manufacturing Systems Engineering and the School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China

Funder

National Natural Science Foundation of China

Natural Science Basic Research Plan in Shaanxi Province of China

China Postdoctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/6352949/10113167.pdf?arnumber=10113167

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