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Unsupervised and Semi-Supervised Learning Ser.: Feature and Dimensionality Reduction for Clustering with Deep Learning by Frederic Ros and Rabia Riad (2024, Hardcover)
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- Buy it nowFeature and Dimensionality Reduction for Clustering with Deep Learning by Ros
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About this product
Product Identifiers
PublisherSpringer
ISBN-103031487427
ISBN-139783031487422
eBay Product ID (ePID)2334459635
Product Key Features
Number of PagesXi, 268 Pages
Publication NameFeature and Dimensionality Reduction for Clustering with Deep Learning
LanguageEnglish
Publication Year2024
SubjectEngineering (General), Intelligence (Ai) & Semantics, General, Telecommunications
TypeTextbook
AuthorFrederic Ros, Rabia Riad
Subject AreaMathematics, Computers, Technology & Engineering
SeriesUnsupervised and Semi-Supervised Learning Ser.
FormatHardcover
Dimensions
Item Weight20.7 Oz
Item Length9.3 in
Item Width6.1 in
Additional Product Features
Dewey Edition23
Number of Volumes1 vol.
IllustratedYes
Dewey Decimal006.32
Table Of ContentIntroduction.- Representation Learning in high dimension.- Review of Feature selection and clustering approaches.- Towards deep learning.- Deep learning architectures for feature extraction and selection.- Unsupervised Deep Feature selection techniques.- Deep Clustering Techniques.- Issues and Challenges.- Conclusion.
SynopsisThis 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.
LC Classification NumberTK5101-5105.9
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