Full Description
This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field.
Contents
1 Multi-Aspect Data Learning: Overview, Challenges and Approaches.- 2 Non-negative Matrix Factorization-Based Multi-Aspect Data Clustering.- 3 NMF and Manifold Learning for Multi-Aspect Data.- 4 Subspace Learning for Multi-Aspect Data.- 5 Spectral Clustering on Multi-Aspect Data.- 6 Learning Consensus and Complementary Information for Multi-Aspect Data Clustering.- 7 Deep Learning-Based Methods for Multi-Aspect Data Clustering.
