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Feature and dimensionality reduction for clustering with deep learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Feature and dimensionality reduction for clustering with deep learning/ by Frederic Ros, Rabia Riad.
Author:	Ros, Frederic.
other author:	Riad, Rabia.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xi, 268 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning) -
Online resource:	https://doi.org/10.1007/978-3-031-48743-9
ISBN:	9783031487439

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

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

3538509
Deep learning (Machine learning)

LC Class. No.: Q325.73

Dewey Class. No.: 006.31

Feature and dimensionality reduction for clustering with deep learning
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300 $a xi, 268 p. : $b ill., digital ; $c 24 cm.
490 1 $a Unsupervised and semi-supervised learning, $x 2522-8498
505 0 $a 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.
520 $a 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.
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650 1 4 $a Communications Engineering, Networks. $3 891094
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650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Automated Pattern Recognition. $3 3538549
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