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Matrix Factorization for Multimedia Clustering: Models, Techniques, Optimization and Applications

Name: Matrix Factorization for Multimedia Clustering: Models, Techniques, Optimization and Applications
Brand: Institution of Engineering & Technology
SKU: 9781837241996
Price: 120.99 USD
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Contributors:

Hangjun Che, Xin Wang, Xing He

Publisher:

Institution of Engineering & Technology

Binding:

Hardcover

Book Category:

Computers

Book Subcategory:

Computer Science

Series:

Computing and Networks

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The product is eligible for a complimentary return within 30 days from the date of purchase. Furthermore, individual items are shipped within 24 hours of receiving the order.

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Release Date:

12/31/2025

ISBN 13:

9781837241996

Weight:

1.23 lbs

Size:

9.21 x 6.14 x 0.63 inches

Pages:

269

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product description

Description

Clustering is a fundamental problem in multimedia information processing. This co-authored book explores clustering principles through advanced data analysis techniques, such as matrix and tensor factorization, which are highly relevant for multimedia information processing. Multimedia data may exhibit various forms of noise represented from multiple perspectives, making traditional clustering approaches less effective. The authors consider complex conditions such as noise sensitivity and discuss methods to address these challenges in the context of multimedia data. They also examine popular regularization techniques, providing theoretical analyses that demonstrate the relationship between regularization and clustering performance.

Matrix Factorization for Multimedia Clustering: Models, techniques, optimization and applications will serve as a solid advanced reference for researchers, scientists, engineers and advanced students who wish to implement practical tasks through clustering formulations. Additionally, the authors provide a detailed description of convergence theory to enable readers to conduct the corresponding algorithm analyses. They investigate novel regularization techniques, such as self-paced learning, optimal graph learning, and diversity regularization, to uncover the geometric structure of data. These techniques are beneficial for enhancing clustering performance. Furthermore, they demonstrate the efficiency of these regularization techniques through theoretical analyses, practical experiments and applications in real-world datasets.

About the Author
Leung, Man-Fai: -

Man-Fai Leung is a lecturer with the School of Computing and Information Science in the Faculty of Science and Engineering at Anglia Ruskin University, Cambridge, UK. His research interests include intelligent systems, optimization, computational intelligence, and their applications. He serves as an associate editor for Complex & Intelligent Systems, and Intelligent Systems with Applications. He has served as the publications chair for the 10th, 11th, and 13th International Conference on Information Science and Technology. He is a member of IEEE. He received his PhD degree in computer science from the City University of Hong Kong, China.

Wang, Xin: -

Xin Wang is a professor at the School of Electronic and Information Engineering, Southwest University, China. His current research interests include complex networks, impulsive control, multi-agent systems, and adaptive control. He has published over 60 papers in authoritative journals and conference papers and is a regular journal reviewer. He is a member of IEEE and CAA. He obtained his PhD degree in computer science and technology from Chongqing University, China.

He, Xing: -

Xing He is a professor with the College of Electronic and Information Engineering, Southwest University, China. He focuses his research on neural networks and bifurcation theory. He has published over 100 papers and is a regular journal reviewer. He is a senior member of IEEE. He obtained his PhD degree in computer science and technology from Chongqing University, China.

Et al...

Matrix Factorization for Multimedia Clustering: Models, Techniques, Optimization and Applications

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