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Multi-aspect Learning: Methods and Applications

By (author) Richi Nayak, By (author) Khanh Luong

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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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The Coming Wave: AI, Power and Our Future

Mustafa Suleyman
Paperback / softback
352 pages
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The Order of Time

Carlo Rovelli
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224 pages
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Originally published in Italian: L'ordine del tempo (Milan: Adelphi Edizioni, 2017).

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Helgoland: The Strange and Beautiful Story of Quantum Physics

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