Dimensionality Reduction in Data Science

Edited by Max Garzon (), Ching-Chi Yang (), Deepak Venugopal (), Nirman Kumar (), Kalidas Jana () and Lih-Yuan Deng ()

in Springer Books from Springer

Date: 2022
ISBN: 978-3-031-05371-9
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Chapters in this book:

Ch Chapter 1 What Is Data Science (DS)?

Max Garzon, Ching-Chi Yang and Lih-Yuan Deng

Ch Chapter 10 Metaheuristics of DR Methods

Deepak Venugopal, Max Garzon, Nirman Kumar, Ching-Chi Yang and Lih-Yuan Deng

Ch Chapter 11 Appendices

Max Garzon, Lih-Yuan Deng, Nirman Kumar, Deepak Venugopal, Kalidas Jana and Ching-Chi Yang

Ch Chapter 2 Solutions to Data Science Problems

Deepak Venugopal, Lih-Yuan Deng and Max Garzon

Ch Chapter 3 What Is Dimensionality Reduction (DR)?

Lih-Yuan Deng, Max Garzon and Nirman Kumar

Ch Chapter 4 Conventional Statistical Approaches

Ching-Chi Yang, Max Garzon and Lih-Yuan Deng

Ch Chapter 5 Geometric Approaches

Nirman Kumar

Ch Chapter 6 Information-Theoretic Approaches

Max Garzon, Sambriddhi Mainali and Kalidas Jana

Ch Chapter 7 Molecular Computing Approaches

Max Garzon and Sambriddhi Mainali

Ch Chapter 8 Statistical Learning Approaches

Ching-Chi Yang and Lih-Yuan Deng

Ch Chapter 9 Machine Learning Approaches

Deepak Venugopal and Max Garzon

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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprbok:978-3-031-05371-9

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DOI: 10.1007/978-3-031-05371-9

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