Feature and Dimensionality Reduction for Clustering with Deep Learning

This book explores advanced methods for feature selection and dimensionality reduction using Deep Neural Networks, focusing on clustering and knowledge discovery.

This book provides a comprehensive overview of the latest techniques in feature selection and dimensionality reduction that utilize Deep Neural Networks (DNNs) from a clustering perspective. It particularly addresses the critical issue of knowledge discovery, making it a valuable resource for those looking to deepen their understanding of this evolving field. The authors begin by synthesizing the key techniques and innovative approaches that have driven recent advancements in deep clustering, alongside a review of the principal deep learning architectures involved.

In addition to outlining foundational concepts, the book categorizes the most influential works by 'family', offering readers a structured starting point to navigate the complexities of the domain. By grouping similar methodologies, the authors facilitate a clearer understanding of how various techniques interrelate and contribute to the broader landscape of deep learning applications in clustering.

Overall, Feature and Dimensionality Reduction for Clustering with Deep Learning serves as an up-to-date reference for deep feature selection and deep clustering methods, emphasizing knowledge discovery through a multi-criteria analysis. This makes it particularly beneficial for young researchers, non-experts, and R&D AI engineers who are eager to engage with contemporary challenges and innovations in the field.