An Outlier-Resilient Autoencoder for Representing High-Dimensional and Incomplete Data-Reference-Cited by-同舟云学术

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An Outlier-Resilient Autoencoder for Representing High-Dimensional and Incomplete Data

Published:2024 Issue: Volume: Page:1-13
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Wu Di¹^ORCID,Hu Yuanpeng²,Liu Kechen³,Li Jing²^ORCID,Wang Xianmin²^ORCID,Deng Song⁴^ORCID,Zheng Nenggan⁵^ORCID,Luo Xin¹^ORCID

Affiliation:

1. College of Computer and Information Science, Southwest University, Chongqing, China

2. School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China

3. Department of Computer Science, Columbia University, New York, NY, USA

4. Institute of Advanced Technology, Nanjing University Post & Telecommunication, Nanjing, China

5. Qiushi Academy for Advanced Studies (QAAS), Zhejiang University Hangzhou, Zhejiang Province, China

Funder

National Natural Science Foundation of China

Chongqing Technical Innovation and Application Development Special Project

Chongqing Natural Science Foundation

Natural Science Foundation of Guangdong Province

Science and Technology Program of Guangzhou, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/7433297/7777658/10634222.pdf?arnumber=10634222

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