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. - 1st ed. 2024. - XI, 268 p. 1 illus. online resource. - 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.
9783031487439
Telecommunication.
Computational intelligence.
Data mining.
Pattern recognition systems.
Communications Engineering, Networks.
Computational Intelligence.
Data Mining and Knowledge Discovery.
Automated Pattern Recognition.
TK5101-5105.9
621.382
Feature and Dimensionality Reduction for Clustering with Deep Learning [electronic resource] / by Frederic Ros, Rabia Riad. - 1st ed. 2024. - XI, 268 p. 1 illus. online resource. - 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.
9783031487439
Telecommunication.
Computational intelligence.
Data mining.
Pattern recognition systems.
Communications Engineering, Networks.
Computational Intelligence.
Data Mining and Knowledge Discovery.
Automated Pattern Recognition.
TK5101-5105.9
621.382
