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03884nam a2200565 a 4500 |
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cr bn |||m|||a |
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081019s2006 caua fsb 000 0 eng d |
| 020 |
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|a 1598290312 (electronic bk.)
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| 020 |
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|a 9781598290318 (electronic bk.)
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|a 1598290304 (pbk.)
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|a 9781598290301 (pbk.)
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7 |
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|a 10.2200/S00016ED1V01Y200602SPR002
|2 doi
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| 035 |
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|a (OCoLC)65338939
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| 035 |
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|a (CaBNvSL)gtp00531440
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|a CaBNvSL
|c CaBNvSL
|d CaBNvSL
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4 |
|a TK5102.9
|b .A45 2006
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| 082 |
0 |
4 |
|a 621.3822
|2 22
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| 100 |
1 |
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|a Almeida, L. B.
|q (Luis B.)
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| 245 |
1 |
0 |
|a Nonlinear source separation
|c Luis B. Almeida.
|h [electronic resource] /
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| 250 |
# |
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|a 1st ed.
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| 260 |
# |
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|a San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) :
|b Morgan & Claypool Publishers,
|c c2006.
|
| 300 |
# |
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|a 1 electronic text (xii, 101 p. : ill.) :
|b digital file.
|
| 490 |
1 |
# |
|a Synthesis lectures on signal processing,
|v #2
|x 1932-1694 ;
|
| 500 |
# |
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|a Part of: Synthesis digital library of engineering and computer science.
|
| 500 |
# |
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|a Title from PDF t.p. (viewed Oct. 19, 2008).
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| 500 |
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|a Series from website.
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| 504 |
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|a Includes bibliographical references (p. 89-99).
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| 505 |
0 |
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|a 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.
|
| 506 |
# |
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|a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
|
| 510 |
0 |
# |
|a Compendex
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| 510 |
0 |
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|a INSPEC
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| 510 |
0 |
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|a Google scholar
|
| 510 |
0 |
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|a Google book search
|
| 520 |
# |
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|a 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.
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| 530 |
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|a Also available in print.
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| 538 |
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|a Mode of access: World Wide Web.
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| 538 |
# |
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|a System requirements: Adobe Acrobat Reader.
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| 650 |
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0 |
|a Blind source separation.
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| 650 |
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0 |
|a Nonlinear theories.
|
| 690 |
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|a Signal processing.
|
| 690 |
# |
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|a Source separation.
|
| 690 |
# |
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|a Nonlinear blind source separation.
|
| 690 |
# |
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|a Independent component analysis.
|
| 690 |
# |
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|a Nonlinear ICA.
|
| 730 |
0 |
# |
|a Synthesis digital library of engineering and computer science.
|
| 830 |
# |
0 |
|a Synthesis lectures on signal processing (Online),
|v #2.
|x 1932-1694 ;
|
| 856 |
4 |
2 |
|u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.2200/S00016ED1V01Y200602SPR002
|3 Abstract with links to full text
|