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Information Fusion and Data Science

Feature Learning and Understanding

Algorithms and Applications

Authors: Zhao, H., Lai, Z., Leung, H., Zhang, X.

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  • Offers advanced feature learning methods, such as sparse learning, and deep-learning-based feature learning 
  • Includes also traditional and cutting-edge feature learning methods
  • Contains the detailed theoretical analysis of each feature learning method
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eBook $109.00
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  • ISBN 978-3-030-40794-0
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Hardcover $149.99
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  • ISBN 978-3-030-40793-3
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About this book

This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence.


About the authors

Haitao Zhao is currently a full professor at the School of Information Science and Engineering, East China University of Science and Technology (ECUST), Shanghai, China. His research interests include feature extraction, representation learning, feature fusion, classifier design and their applications in image processing and computer vision.

Henry Leung is a professor of the Department of Electrical and Computer Engineering of the University of Calgary. His current research interests include information fusion, machine learning, IoT, nonlinear dynamics, robotics, signal and image processing. He is a Fellow of IEEE and SPIE. 

Zhihui Lai was a Postdoctoral Fellow at the Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology (HIT) in 2011-2013. He is now a full professor at the College of Computer Science and Software Engineering, Shenzhen University.

Xianyi Zhang
is a postgraduate at the School of Information Science and Engineering, East China University of Science and Technology (ECUST), Shanghai, China. His research interests include pattern recognition, machine learning and image processing.


Table of contents (12 chapters)

Table of contents (12 chapters)

Buy this book

eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-030-40794-0
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $149.99
price for USA in USD
  • ISBN 978-3-030-40793-3
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Feature Learning and Understanding
Book Subtitle
Algorithms and Applications
Authors
Series Title
Information Fusion and Data Science
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-40794-0
DOI
10.1007/978-3-030-40794-0
Hardcover ISBN
978-3-030-40793-3
Series ISSN
2510-1528
Edition Number
1
Number of Pages
XIV, 291
Number of Illustrations
17 b/w illustrations, 109 illustrations in colour
Topics

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