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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.
Inhaltsverzeichnis
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.
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