03432nam 22005895 450 991073602330332120230727113449.03-031-33560-010.1007/978-3-031-33560-0(MiAaPQ)EBC30668291(Au-PeEL)EBL30668291(DE-He213)978-3-031-33560-0(PPN)272251798(CKB)27867633400041(EXLCZ)992786763340004120230727d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMulti-aspect Learning Methods and Applications /by Richi Nayak, Khanh Luong1st ed. 2023.Cham :Springer International Publishing :Imprint: Springer,2023.1 online resource (191 pages)Intelligent Systems Reference Library,1868-4408 ;242Print version: Nayak, Richi Multi-Aspect Learning Cham : Springer International Publishing AG,c2023 9783031335594 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.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.Intelligent Systems Reference Library,1868-4408 ;242Engineering—Data processingComputational intelligenceMachine learningData EngineeringComputational IntelligenceMachine LearningEngineering—Data processing.Computational intelligence.Machine learning.Data Engineering.Computational Intelligence.Machine Learning.620.00285Nayak Richi1361180Luong Khanh1380287MiAaPQMiAaPQMiAaPQBOOK9910736023303321Multi-Aspect Learning3421568UNINA