Overview
- Latest research on the theoretical foundations and applications of subspace methods for pattern recognition using intelligent techniques
Part of the book series: Studies in Computational Intelligence (SCI, volume 552)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (8 chapters)
-
Front Matter
-
Back Matter
Keywords
About this book
This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.
Editors and Affiliations
Bibliographic Information
Book Title: Subspace Methods for Pattern Recognition in Intelligent Environment
Editors: Yen-Wei Chen, Lakhmi C. Jain
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-642-54851-2
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2014
Hardcover ISBN: 978-3-642-54850-5Published: 22 April 2014
Softcover ISBN: 978-3-662-50190-0Published: 03 September 2016
eBook ISBN: 978-3-642-54851-2Published: 07 April 2014
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XVI, 199
Number of Illustrations: 47 b/w illustrations, 52 illustrations in colour
Topics: Mathematical and Computational Engineering, Artificial Intelligence, Pattern Recognition
Industry Sectors: Aerospace, Automotive, Biotechnology, Electronics, Energy, Utilities & Environment, Engineering, IT & Software, Oil, Gas & Geosciences, Telecommunications