Multi-aspect learning : methods and applications / Richi Nayak, Khanh Luong.

Unlimited

Authorized users

Can lend chapters, not whole ebooks

Multi-aspect learning : methods and applications / Richi Nayak, Khanh Luong.

9783031335600 (electronic bk.)
3031335600 (electronic bk.)
9783031335594
3031335597

Cham : Springer, 2023.

English

1 online resource (viii, 184 pages) : illustrations (some color).

10.1007/978-3-031-33560-0 doi

Q325.5

006.3/1

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.

Includes bibliographical references.

Access limited to authorized users.

Online resource; title from PDF title page (SpringerLink, viewed August 1, 2023).

Intelligent systems reference library ; v. 242.

Print version: 9783031335594

Online Resources > Ebooks
All Resources