Feature Learning and Understanding: Algorithms and Applications

By Henry Leung, Zhihui Lai, Haitao Zhao

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.

• Format: Hardcover
• Language: English
• ISBN: 3030407934
• ISBN13: 9783030407933
• Release Date: April 2020
• Publisher: Springer
• Length: 291 Pages
• Weight: 1.34 lbs.
• Dimensions: 0.8" x 6.1" x 9.2"