Dynamic Graph Learning for Dimension Reduction and Data Clustering (Synthesis Lectures on Computer Science) - Hardcover

Zhu, Lei; Zhang, Zheng; Li, Jingjing

Hardcover

ISBN 10: 3031423127 ISBN 13: 9783031423123

Verlag: Springer-Verlag GmbH, 2023

Inhaltsangabe

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.

Über diesen Titel

Über die Autorin bzw. den Autor

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 morethan 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.

Von der hinteren Coverseite

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.

Details zur Ausgabe

• Verlag: Springer-Verlag GmbH
• Erscheinungsdatum: 2023
• Sprache: Englisch
• ISBN 10: 3031423127
• ISBN 13: 9783031423123
• Einband: Tapa dura
• Anzahl der Seiten: 168
• Kontakt zum Hersteller: Nicht verfügbar