Information Fusion and Data Science: Algorithms and Applications

Zhao, Haitao; Lai, Zhihui; Leung, Henry; Zhang, Xianyi

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.

Details

Published by: Springer

Publication Date: 2020-04-04

Format: Hardcover

ISBN-13: 9783030407933

DOI: 10.1007/978-3-030-40794-0

Dimensions: 235cm x155cm

Pages: 291