Feature Learning and Understanding : Algorithms and Applications / by Haitao Zhao, Zhihui Lai, Henry Leung, Xianyi Zhang.

Format:

Book

Author/Creator:

Zhao, Haitao, 1986- author.

Lai, Zhihui, author.

Leung, Henry, author.

Zhang, Xianyi, author.

Contributor:

SpringerLink (Online service)

Series:

Physics and Astronomy (Springer-11651)

Information Fusion and Data Science,. 2510-1528

Information Fusion and Data Science, 2510-1528

Language:

English

Subjects (All):

Sociophysics.

Econophysics.

Machine learning.

Computational intelligence.

Pattern perception.

Signal processing.

Image processing.

Speech processing systems.

Optical data processing.

Data-driven Science, Modeling and Theory Building.

Machine Learning.

Computational Intelligence.

Pattern Recognition.

Signal, Image and Speech Processing.

Image Processing and Computer Vision.

Local Subjects:

Data-driven Science, Modeling and Theory Building.

Machine Learning.

Computational Intelligence.

Pattern Recognition.

Signal, Image and Speech Processing.

Image Processing and Computer Vision.

Physical Description:

1 online resource (XIV, 291 pages) : 126 illustrations, 109 illustrations in color.

Edition:

First edition 2020.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

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.

Contents:

Chapter1. A Gentle Introduction to Feature Learning

Chapter2. Latent Semantic Feature Learning

Chapter3. Principal Component Analysis

Chapter4. Local-Geometrical-Structure-based Feature Learning

Chapter5. Linear Discriminant Analysis

Chapter6. Kernel-based nonlinear feature learning

Chapter7. Sparse feature learning

Chapter8. Low rank feature learning

Chapter9. Tensor-based Feature Learning

Chapter10. Neural-network-based Feature Learning: Autoencoder

Chapter11. Neural-network-based Feature Learning: Convolutional Neural Network

Chapter12. Neural-network-based Feature Learning: Recurrent Neural Network.

Other Format:

Printed edition:

ISBN:

978-3-030-40794-0

9783030407940

Access Restriction:

Restricted for use by site license.