Independent Component Analysis (Hardcover)
Aapo Hyvärinen, Juha Karhunen, Erkki Oja
- 出版商: Wiley-Interscience
- 出版日期: 2001-05-18
- 售價: $1,460
- 貴賓價: 9.5 折 $1,387
- 語言: 英文
- 頁數: 504
- 裝訂: Hardcover
- ISBN: 047140540X
- ISBN-13: 9780471405405
貴賓價: $902Computer Science Illuminated, 2/e
貴賓價: $970Introduction to Algorithms, 2/e
貴賓價: $1,045Operating System Principles, 7/e(IE) (美國版ISBN:0471694665－Operating System Concepts, 7/e) (平裝)
貴賓價: $1,539Data Structures and Algorithms Made Easy: Data Structure and Algorithmic Puzzles, 2/e (Paperback)(快遞進口)
貴賓價: $1,140Computer Organization and Design: The Hardware/Software Interface, 3/e(IE) (美國版ISBN:1558606041)
貴賓價: $1,984CCNA Self-Study: Interconnecting Cisco Network Devices (ICND) 640-811, 640-801, 2/e
Independent Component Analysis (ICA) is one of the most exciting new topics in fields such as neural networks, advanced statistics, and signal processing. This is the first book to provide a comprehensive introduction to this new technique complete with the fundamental mathematical background needed to understand and utilize it. It offers a general overview of the basics of ICA, important solutions and algorithms, and in-depth coverage of new applications in image processing, telecommunications, audio signal processing, and more.
Independent Component Analysis is divided into four sections that cover:
- General mathematical concepts utilized in the book
- The basic ICA model and its solution
- Various extensions of the basic ICA model
- Real-world applications for ICA models
Authors Hyvärinen, Karhunen, and Oja are well known for their contributions to the development of ICA and here cover all the relevant theory, new algorithms, and applications in various fields. Researchers, students, and practitioners from a variety of disciplines will find this accessible volume both helpful and informative.
Table of Contents
Random Vectors and Independence.
Gradients and Optimization Methods.
Principal Component Analysis and Whitening.
BASIC INDEPENDENT COMPONENT ANALYSIS.
What is Independent Component Analysis?
ICA by Maximization of Nongaussianity.
ICA by Maximum Likelihood Estimation.
ICA by Minimization of Mutual Information.
ICA by Tensorial Methods.
ICA by Nonlinear Decorrelation and Nonlinear PCA.
Overview and Comparison of Basic ICA Methods.
EXTENSIONS AND RELATED METHODS.
ICA with Overcomplete Bases.
Methods using Time Structure.
Convolutive Mixtures and Blind Deconvolution.
APPLICATIONS OF ICA.
Feature Extraction by ICA.
Brain Imaging Applications.