Feature and Dimensionality Reduction for Clustering with Deep Learning (Unsupervised and Semi-supervised Learning)

Ros, Frederic/ Riad, Rabia

Springer International Publishing AG（2025/01発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 268 p.
• 言語 ENG
• 商品コード 9783031487453

Full Description

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.

Contents

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.