Dynamic Graph Learning for Dimension Reduction and Data Clustering

Lei Zhu|Jingjing Li|Zheng Zhang

ISBN 10: 3031423127 / ISBN 13: 9783031423123

Editore: Springer Nature Switzerland, 2023

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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides thorough background information on dynamic graph learning and the recent advancements in the areaDescribes the benefits of utilizing dynamic graph learning, as compared to a fixed graph approachCovers several practical applications.

Codice articolo 945398625

Dati bibliografici

• Titolo: Dynamic Graph Learning for Dimension Reduction and Data Clustering
• Editore: Springer Nature Switzerland
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 3031423127
• ISBN 13: 9783031423123
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

This book illustrates how to achieve effective dimension reduction and data clustering. The authors explain how to accomplish this by utilizing the advanced dynamic graph learning technique in the era of big data. The book begins by providing background on dynamic graph learning. The authors discuss why it has attracted considerable research attention in recent years and has become well recognized as an advanced technique. After covering the key topics related to dynamic graph learning, the book discusses the recent advancements in the area. The authors then explain how these techniques can be practically applied for several purposes, including feature selection, feature projection, and data clustering.

Informazioni sull?autore

Lei Zhu, PhD., is a Professor in the School of Information Science and Engineering at Shandong Normal University. He received his B.Eng. and Ph.D. degrees from Wuhan University of Technology in 2009 and Huazhong University Science and Technology in 2015, respectively. He was also previously a Research Fellow at the University of Queensland. Zhu has co-/authored more than 100 peer-reviewed papers, such as ACM SIGIR, ACM MM, IEEE TPAMI, IEEE TIP, IEEE TKDE, and ACM TOIS. He won ACM SIGIR 2019 Best Paper Honorable Mention Award, ADMA 2020 Best Paper Award, ACM China SIGMM Rising Star Award. His research interests are in the area of big data mining and large-scale multimedia content analysis and retrieval.

Jingjing Li, PhD., is a Professor in the School of Computer Science and Engineering at the University of Electronic Science and Technology of China (UESTC). He received his B.Eng., M.Sc. and Ph.D. degrees from UESTC in 2010, 2013 and 2015, respectively. He has co-/authored more than 70 peer-reviewed papers, such as IEEE TPAMI, IEEE TIP, IEEE TKDE, CVPR, ICCV, AAAI, IJCAI and ACM Multimedia. He won Excellent Doctoral Dissertation award of Chinese Institute of Electronics in 2018. His research interests are in the area of domain adaptation and zero-shot learning.

Zheng Zhang, PhD., is a tenured Associate Professor at the School of Computer Science & Technology, Harbin Institute of Technology, Shenzhen, China. He received his Ph.D. degree in Computer Applied Technology from Harbin Institute of Technology in 2018. He has published over 150 technical papers in prestigious journals and conferences, such as IEEE TPAMI, IJCV, IEEE TIP, IEEE TNNLS, CVPR, ECCV, ICCV, ACM MM, AAAI, and IJCAI. He has received the 2019 Young Outstanding Research Achievement Award of the Chinese Association for Artificial Intelligence (CAAI) and was also a recipient of the "Honorable Mentioned Award" from ACM Multimedia Asia 2021 and the "Best Paper Award" from International Conference on Smart Computing 2014. His research interests include machine learning, computer vision, and multimedia analytics.