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Feature and Dimensionality Reduction for Clustering With Deep Learning

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Feature and Dimensionality Reduction for Clustering With Deep Learning Synopsis

This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by "family" to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.


About This Edition

ISBN: 9783031487453
Publication date:
Author: Frederic Ros, Rabia Riad
Publisher: Springer an imprint of Springer Nature Switzerland
Format: Paperback
Pagination: 268 pages
Series: Unsupervised and Semi-Supervised Learning
Genres: Communications engineering / telecommunications
Expert systems / knowledge-based systems
Pattern recognition
Data mining
Artificial intelligence

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