Feature and Dimensionality Reduction for Clustering with Deep Learning

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 infl...

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書誌詳細
主要な著者:	Ros, Frederic (著者), Riad, Rabia (著者)
団体著者:	SpringerLink (Online service)
フォーマット:	電子媒体 eBook
言語:	英語
出版事項:	Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
版:	1st ed. 2024.
シリーズ:	Unsupervised and Semi-Supervised Learning,
主題:	Telecommunication. Computational intelligence. Data mining. Pattern recognition systems. Communications Engineering, Networks. Computational Intelligence. Data Mining and Knowledge Discovery. Automated Pattern Recognition.
オンライン･アクセス:	https://doi.org/10.1007/978-3-031-48743-9
タグ:	タグ追加タグなし, このレコードへの初めてのタグを付けませんか!

目次:

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.

Feature and Dimensionality Reduction for Clustering with Deep Learning

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