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...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer Nature Switzerland
2024, 2024
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| Edition: | 1st ed. 2024 |
| Series: | Unsupervised and Semi-Supervised Learning
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of 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