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Feature and dimensionality reduction for clustering with deep learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Feature and dimensionality reduction for clustering with deep learningby Frederic Ros, Rabia Riad.
作者:	Ros, Frederic.
其他作者:	Riad, Rabia.
出版者:	Cham :Springer Nature Switzerland :2024.
面頁冊數:	xi, 268 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Deep learning (Machine learning)
電子資源:	https://doi.org/10.1007/978-3-031-48743-9
ISBN:	9783031487439$q(electronic bk.)

Feature and dimensionality reduction for clustering with deep learning
Ros, Frederic.

Feature and dimensionality reduction for clustering with deep learning[electronic resource] /by Frederic Ros, Rabia Riad. - Cham :Springer Nature Switzerland :2024. - xi, 268 p. :ill., digital ;24 cm. - Unsupervised and semi-supervised learning,2522-8498. - Unsupervised and semi-supervised learning..

Introduction -- 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.

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. Presents a synthesis of recent influencing techniques and "tricks" participating in advances in deep clustering; Highlights works by "family" to provide a more suitable starting point to develop a full understanding of the domain; Includes recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks.

ISBN: 9783031487439$q(electronic bk.)

Standard No.: 10.1007/978-3-031-48743-9doiSubjects--Topical Terms:

913129
Deep learning (Machine learning)

LC Class. No.: Q325.73

Dewey Class. No.: 006.31

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