TY  - BOOK
AU  - Ros,Frederic
AU  - Riad,Rabia
ED  - SpringerLink (Online service)
TI  - Feature and Dimensionality Reduction for Clustering with Deep Learning
T2  - Unsupervised and Semi-Supervised Learning,
SN  - 9783031487439
AV  - TK5101-5105.9 
U1  - 621.382 23
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Telecommunication
KW  - Computational intelligence
KW  - Data mining
KW  - Pattern recognition systems
KW  - Communications Engineering, Networks
KW  - Computational Intelligence
KW  - Data Mining and Knowledge Discovery
KW  - Automated Pattern Recognition
N1  - 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
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-48743-9
ER  -