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Feature and Dimensionality Reduction for Clustering with Deep Learning

Medium: Buch
ISBN: 978-3-031-48742-2
Verlag: Springer Nature Switzerland
Erscheinungstermin: 03.01.2024
Lieferfrist: bis zu 10 Tage

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.


Produkteigenschaften


  • Artikelnummer: 9783031487422
  • Medium: Buch
  • ISBN: 978-3-031-48742-2
  • Verlag: Springer Nature Switzerland
  • Erscheinungstermin: 03.01.2024
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2024
  • Serie: Unsupervised and Semi-Supervised Learning
  • Produktform: Gebunden
  • Gewicht: 588 g
  • Seiten: 268
  • Format (B x H x T): 160 x 241 x 21 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

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.