Robust and Sparse Principal Component Analysis With Adaptive Loss Minimization for Feature Selection-Reference-Cited by-同舟云学术

Robust and Sparse Principal Component Analysis With Adaptive Loss Minimization for Feature Selection

Published:2022 Issue: Volume: Page:1-14
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Bian Jintang¹^ORCID,Zhao Dandan²^ORCID,Nie Feiping³^ORCID,Wang Rong⁴^ORCID,Li Xuelong³

Affiliation:

1. School of Computer Science and the Center for Optical Imagery Analysis and Learning, Northwestern Polytechnical University, Xi’an, China

2. College of Automation, Chongqing University, Chongqing, China

3. School of Computer Science and the School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, China

4. School of Cybersecurity and the School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, China

Funder

National Key Research and Development Program of China

Natural Science Basic Research Program of Shaanxi

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/09870686.pdf?arnumber=9870686

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