Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Feature and Dimensionality Reduction for Clustering with Deep Learning

Feature and Dimensionality Reduction for Clustering with Deep Learning

Ros, Frederic
Riad, Rabia

Hardcover

Series: Unsupervised and Semi-Supervised Learning

Technology & EngineeringDatabases

ISBN10: 3031487427
ISBN13: 9783031487422
Publisher: Springer Nature
Published: Jan 3 2024
Pages: 268
Weight: 1.26
Height: 0.69 Width: 6.14 Depth: 9.21
Language: English

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.

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