Multi-aspect Learning [electronic resource] : Methods and Applications / by Richi Nayak, Khanh Luong.

Por: Nayak, Richi [author.]Colaborador(es): Luong, Khanh [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Intelligent Systems Reference Library ; 242Editor: Cham : Springer International Publishing : Imprint: Springer, 2023Edición: 1st ed. 2023Descripción: VIII, 184 p. 71 illus., 70 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783031335600Tema(s): Engineering -- Data processing | Computational intelligence | Machine learning | Data Engineering | Computational Intelligence | Machine LearningFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 620.00285 Clasificación LoC:TA345-345.5Recursos en línea: Libro electrónicoTexto
Contenidos:
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.
En: Springer Nature eBookResumen: 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.
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Acceso multiusuario

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.

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