| Author |
Almeida, Luis Bigotte de
|
| Title |
Nonlinear source separation / Luis B. Almeida. |
| Edition |
First edition. |
| OCLC |
200602SPR002 |
| ISBN |
1598290312 (electronic bk.) |
|
9781598290318 (electronic bk.) |
|
1598290304 (pbk.) |
|
9781598290301 (pbk.) |
| ISBN/ISSN |
10.2200/S00016ED1V01Y200602SPR002 doi |
| Publisher |
San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, [2006] |
|
©2006 |
| Description |
1 electronic text (xii, 101 pages : illustrations\.) : digital file. |
| LC Subject heading/s |
Blind source separation.
|
|
Nonlinear theories.
|
| SUBJECT |
Signal processing.
|
|
Source separation.
|
|
Nonlinear blind source separation.
|
|
Independent component analysis.
|
|
Nonlinear ICA.
|
| System details note |
Mode of access: World Wide Web. |
|
System requirements: Adobe Acrobat Reader. |
| Bibliography |
Includes bibliographical references (pages 89-99). |
| Contents |
Acknowledgments -- Notation -- Preface -- 1. Introduction. -- 1.1. Basic concepts -- 1.2. Summary -- 2. Linear source separation -- 2.1. Statement of the problem -- 2.2. INFOMAX -- 2.3. Exploiting the time-domain structure -- 2.4. Other methods : JADE and FastICA -- 2.5. Summary -- 3. Nonlinear separation -- 3.1. Post-nonlinear mixtures -- 3.2. Unconstrained nonlinear separation -- 3.3. Conclusion -- 4. Final comments -- A. Statistical concepts -- A.1. Passing a random variable through its cumulative distribution function -- A.2. Entropy -- A.3. Kullback-Leibler divergence -- A.4. Mutual information -- B. Online software and data. |
| Restrictions |
Abstract freely available; full-text restricted to subscribers or individual document purchasers. |
|
Access may be restricted to authorized users only. |
|
Unlimited user license access |
| NOTE |
Compendex. |
|
INSPEC. |
|
Google book search. |
| Summary |
The purpose of this lecture book is to present the state of the art in nonlinear blind source separation, in a form appropriate for students, researchers and developers. Source separation deals with the problem of recovering sources that are observed in a mixed condition. When we have little knowledge about the sources and about the mixture process, we speak of blind source separation. Linear blind source separation is a relatively well studied subject. Nonlinear blind source separation is still in a less advanced stage, but has seen several significant developments in the last few years. This publication reviews the main nonlinear separation methods, including the separation of post-nonlinear mixtures, and the MISEP, ensemble learning and kTDSEP methods for generic mixtures. These methods are studied with a significant depth. A historical overview is also presented, mentioning most of the relevant results, on nonlinear blind source separation, that have been presented over the years. |
| NOTE |
Google scholar. |
| Additional physical form available note |
Also available in print. |
| General note |
Part of: Synthesis digital library of engineering and computer science. |
|
Title from PDF t.p. (viewed Oct. 19, 2008). |
|
Series from website. |
|
