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.

• 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