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Broad Learning Autoencoder With Graph Structure for Data Clustering

Published:2024-01 Issue:1 Volume:36 Page:49-61
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Yu Zhiwen¹^ORCID,Zhong Zhijie²^ORCID,Yang Kaixiang²^ORCID,Cao Wenming³^ORCID,Chen C. L. Philip²^ORCID

Affiliation:

1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China

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

3. Department of Information and Computing Science, Chongqing Jiaotong University, Chongqing, China

Funder

National Natural Science Foundation of China

PCL

Science and Technology Major Project of Guangzhou

Program for Guangdong Introducing Innovative and Entrepreneurial Teams

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/10348031/10144670.pdf?arnumber=10144670

Reference48 articles.

1. Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture

2. Extreme learning machine: Theory and applications

3. Functional-link net computing: theory, system architecture, and functionalities

4. Learning and generalization characteristics of the random vector functional-link net

5. A Fast Reduced Kernel Extreme Learning Machine

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2. Consensus representation-driven structured graph learning for multi-view clustering;Applied Intelligence;2024-06-29

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