Extraordinarily Time-and Memory-Efficient Large-Scale Canonical Correlation Analysis in Fourier Domain: From Shallow to Deep-Reference-Cited by-同舟云学术

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Extraordinarily Time-and Memory-Efficient Large-Scale Canonical Correlation Analysis in Fourier Domain: From Shallow to Deep

Published:2023 Issue: Volume: Page:1-15
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Shen Xiang-Jun¹^ORCID,Xu Zhaorui¹^ORCID,Wang Liangjun¹,Li Zechao²^ORCID,Liu Guangcan³^ORCID,Fan Jianping⁴^ORCID,Zha ZhengJun⁵^ORCID

Affiliation:

1. Department of Computer Engineering, Jiangsu University, Zhenjiang, China

2. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China

3. School of Automation, Southeast University, Nanjing, China

4. AI Laboratory, Lenovo Research, Beijing, China

5. Department of Automation, University of Science and Technology of China (USTC), Hefei, China

Funder

National Natural Science Foundation of China

NSFC

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/10198225.pdf?arnumber=10198225

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