Full 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.
Contents
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 diseases 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
