Matrix Factorization for Multimedia Clustering Models, techniques, optimization and applications
Produktdetaljer
Biografisk notat
Hangjun Che is an associate professor with the College of Electronic and Information Engineering at Southwest University, China. His research focuses on neurodynamic optimization, sparse coding, and nonnegative matrix factorization. He has published 50+ papers and obtained 6 software copyrights and several pending patents. He serves as a regular journal reviewer and is an advisory editorial board member of Data Technologies and Applications, an associate editor of Intelligent Systems with Applications, an editorial board member of Artificial Intelligence and Applications, and Journal of Social Computing. He is a member of IEEE and CCF. He received his Ph.D. degree in computer science from the City University of Hong Kong, China. 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. 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. 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. Baicheng Pan is pursuing his MS degree in information and communication engineering with the College of Electronic and Information Engineering at Southwest University, China. His research interests include optimization theory and applications, machine learning, multi-view clustering, nonnegative matrix factorization, tensor factorization, and deep matrix factorization. He received his BS degree from Huazhong University of Science and Technology, Wuhan, China.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.
This book explores clustering principles through advanced data analysis techniques, such as matrix and tensor factorization in multimedia information processing. The authors present methods to address these challenges, examine popular regularization techniques, and explore the relationship between regularization and clustering performance.
- Chapter 1: Introduction to matrix factorization
- Chapter 2: Preliminary of tensor factorization
- Chapter 3: Graph theory
- Chapter 4: Optimization theory
- Chapter 5: Graph and smoothed l0 regularized non-negative matrix factorization for clustering
- Chapter 6: Self-paced regularized matrix factorization for clustering
- Chapter 7: Centric graph regularized log-norm sparse non-negative matrix factorization for clustering
- Chapter 8: Diversity-constrained matrix factorization for clustering
- Chapter 9: Dual hyper-graph regularized non-negative matrix tri-factorization for clustering
- Chapter 10: Deep matrix factorization for disease detection
- Chapter 11: Matrix factorization for multi-view images clustering
- Chapter 12: Tensor factorization with sparse and graph regularization for fake news detection on social networks
- Chapter 13: Matrix factorization for community detection
- Chapter 14: Conclusion