Learning High-Order Multi-View Representation by New Tensor Canonical Correlation Analysis-Reference-Cited by-同舟云学术

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Learning High-Order Multi-View Representation by New Tensor Canonical Correlation Analysis

Published:2023-10 Issue:10 Volume:33 Page:5645-5654
ISSN:1051-8215
Container-title:IEEE Transactions on Circuits and Systems for Video Technology
language:
Short-container-title:IEEE Trans. Circuits Syst. Video Technol.

Author:

Sun Jianqin¹^ORCID,Xiu Xianchao²^ORCID,Luo Ziyan¹^ORCID,Liu Wanquan³^ORCID

Affiliation:

1. School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, China

2. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China

3. School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

http://xplorestaging.ieee.org/ielx7/76/10269772/10091146.pdf?arnumber=10091146

Reference49 articles.

1. Multiple Kernel Learning Based Multi-view Spectral Clustering

2. A co-training approach for multi-view spectral clustering;kumar;Proc 28th Int Conf Mach Learn,2011

3. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis

4. Kernelized Multiview Subspace Analysis By Self-Weighted Learning

5. Self-supervised Symmetric Nonnegative Matrix Factorization

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Mutual Information-Driven Multi-View Clustering;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21

2. Multiview Latent Structure Learning: Local structure-guided cross-view discriminant analysis;Knowledge-Based Systems;2023-09

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