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Opis

This book provides a comprehensive review and in-depth discussion on multi-aspect data learning, detailing the state-of-the-art representation learning approaches with a focus on clustering and how these unsupervised approaches are applied to various domains and applications of multi-aspect data. The first time the multi-aspect data is reviewed in a systematic manner where various multi-aspect related concepts and a wide range of applications are fully considered. The first time the application of manifold learning used in dimensionality reduction is investigated thoroughly for multi-view data learning. This book thoroughly presents the state-of-the-art approaches to matrix factorization, subspace clustering, spectral clustering and deep learning methods. These approaches are presented in a manner where the main characteristics and challenges of multi-aspect data are the central focus. Each chapter, in addition to providing state-of-the-art of multi-aspect data learning methods, brings forth a comprehensive discussion of important gaps for future work. Each chapter provides readers with a thorough grasp of the baseline information required for them to apply these methods to future domains and applications as well as innovate novel research in this emerging area.   

Szczegóły książki

Kategoria Knihy po anglicky Computing & information technology Databases

• Pełny tytuł: Multi-aspect Learning
• Podtytuł: Methods and Applications
• Autor: Richi Nayak, Khanh Luong
• Język: Angielski
• Oprawa: Twarda
• Liczba stron: 240
• EAN: 9783031335594
• ID: 43298852
• Wydawca: Springer, Berlin
• Wymiary: 235 × 155 mm
• Rok wydania: 2023

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