Adaptive Graph Embedding With Consistency and Specificity for Domain Adaptation

Shaohua Teng; Zefeng Zheng; Naiqi Wu; Luyao Teng; Wei Zhang

doi:10.1109/JAS.2023.123318

Volume 10 Issue 11

Nov. 2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(11): 2094-2107

S. H. Teng, Z. F. Zheng, N. Q. Wu, L. Y. Teng, and W. Zhang, “Adaptive graph embedding with consistency and specificity for domain adaptation,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2094–2107, Nov. 2023. doi: 10.1109/JAS.2023.123318

Citation:

S. H. Teng, Z. F. Zheng, N. Q. Wu, L. Y. Teng, and W. Zhang, “Adaptive graph embedding with consistency and specificity for domain adaptation,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2094–2107, Nov. 2023. doi: 10.1109/JAS.2023.123318

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Adaptive Graph Embedding With Consistency and Specificity for Domain Adaptation

doi: 10.1109/JAS.2023.123318

Funds: This work was supported in part by the Key-Area Research and Development Program of Guangdong Province (2020B010166006), the National Natural Science Foundation of China (61972102), the Guangzhou Science and Technology Plan Project (023A04J1729), and the Science and Technology development fund (FDCT), Macau SAR (015/2020/AMJ)

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Author Bio:
Shaohua Teng (Member, IEEE) received his Ph.D. Degree in Industrial Engineering from Guangdong University of Technology, Guangdong, China, in 2008. He is currently a Professor and Dean at the Department of Artificial Intelligent and Information Engineering, School of Advanced Manufacturing, Guangdong University of Technology, Guangzhou, China. He is a member of Association for Computing Machinery (ACM), a distinguished member of China Computer Federation (CCF), and a senior member of Chinese Association of Automation (CAA). His research interests include Pattern Recognition, Network Security, Collaborative Computing, Artificial Intelligence and its Applications. He is the author of 15 patents on his invention and 300+ journal papers. He has earned Guangdong Excellent Teacher Award and two Provincial Science and Technology Awards. He is a Famous Teacher of Guangdong University of Technology. Dr. Teng is a reviewer of IEEE Transactions on Systems, Man, & Cybernetics: Systems, IEEE/CAA Journal of Automatica Sinica, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Computational Social Systems, and Information Sciences

Zefeng Zheng received the B.S. degree in software engineering from the College of Mathematics and Computer, Guangdong Ocean University, in 2020, and the M.S. degree from the School of Computer Science and Technology, Guangdong University of Technology, in 2023. He is currently pursuing the Ph.D. degree in computer science and technology from the School of Computer Science and Technology, Guangdong University of Technology. His current research interests include machine learning, clustering, and transfer learning. He has served as a Reviewer for a number of journals

NaiQi Wu (Fellow, IEEE) received the B.S. degree in electrical engineering from Anhui University of Technology, in 1982, the M.S. and Ph.D. degrees in systems engineering both from Xi’an Jiaotong University, in 1985 and 1988, respectively. From 1988 to 1995, he was with the Shenyang Institute of Automation, Chinese Academy of Sciences, and from 1995 to 1998, with Shantou University. He moved to Guangdong University of Technology in 1998. He joined Macau University of Science and Technology, Taipa, Macao, China, in 2013. He was a Visiting Professor at Arizona State University, USA, in 1999; New Jersey Institute of Technology, USA, in 2004; University of Technology of Troyes, France, from 2007 to 2009; and Evry University, France, from 2010 to 2011. He is currently a Chair Professor at the Department of Engineering Science and Macao Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macao, China. His research interests include production planning and scheduling, manufacturing system modeling and control, discrete event systems, Petri net theory and applications, intelligent transportation systems, and energy systems. He is the author or coauthor of one book, five book chapters, and 200+ journal papers. Dr. Wu was an Associate Editor of the IEEE Transactions on Systems, Man, & Cybernetics, Part C, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Systems, Man, & Cybernetics: Systems, and Editor in Chief of Industrial Engineering Journal, and is an Associate Editor of Information Sciences

Luyao Teng received the B.S. degree from Monash University, Australia, in 2012, the M.S. degree from University of Melbourne, Australia, in 2014, and the Ph.D. degree from Victoria University, Australia in 2019. She is currently with the School of Information Engineering, Guangzhou Panyu Polytechnic and also with Faculty of Information Technology, Monash University. Her current research interests include pattern recognition and machine learning

Wei Zhang received the M.S. degree in software engineering from the South China University of Technology, in 2005, and is currently a Professor at the School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China. She is a Senior Member of China Computer Federation (CCF). Her research interests include pattern recognition, collaborative computing, artificial intelligence and its applications. She has applied for 11 patents on her invention. She has published 20 papers in international journals. She earned the Provincial Science and Technology Award in 2020
Corresponding author: Luyao Teng, e-mail: tengly@gzpyp.edu.cn
¹ Right hand side
² Left hand side
Received Date: 2022-03-20
Revised Date: 2022-05-12
Accepted Date: 2022-08-20

Available Online: 2023-04-12

Abstract

Abstract

Domain adaptation (DA) aims to find a subspace, where the discrepancies between the source and target domains are reduced. Based on this subspace, the classifier trained by the labeled source samples can classify unlabeled target samples well. Existing approaches leverage Graph Embedding Learning to explore such a subspace. Unfortunately, due to 1) the interaction of the consistency and specificity between samples, and 2) the joint impact of the degenerated features and incorrect labels in the samples, the existing approaches might assign unsuitable similarity, which restricts their performance. In this paper, we propose an approach called adaptive graph embedding with consistency and specificity (AGE-CS) to cope with these issues. AGE-CS consists of two methods, i.e., graph embedding with consistency and specificity (GECS), and adaptive graph embedding (AGE). GECS jointly learns the similarity of samples under the geometric distance and semantic similarity metrics, while AGE adaptively adjusts the relative importance between the geometric distance and semantic similarity during the iterations. By AGE-CS, the neighborhood samples with the same label are rewarded, while the neighborhood samples with different labels are punished. As a result, compact structures are preserved, and advanced performance is achieved. Extensive experiments on five benchmark datasets demonstrate that the proposed method performs better than other Graph Embedding methods.
- Adaptive adjustment,
- consistency and specificity,
- domain adaptation,
- graph embedding,
- geometrical and semantic metrics

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References(45)

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Consistency and specificity components are deeply mined to transfer more knowledge
A graph learning unified framework is built to acquire additional knowledge
An algorithm is implemented to adaptively adjust the significance of consistency and specificity
The proposed method is validated mathematically and empirically

Adaptive Graph Embedding With Consistency and Specificity for Domain Adaptation

doi: 10.1109/JAS.2023.123318

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