Independent component analysis: principles and practice
Independent Component Analysis (ICA) has recently become an important tool for modelling and understanding empirical datasets. It is a method of separating out independent sources from linearly mixed data, and belongs to the class of general linear models. ICA provides a better decomposition than ot...
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| Weitere Verfasser: | , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2014
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| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 UER01 URL des Erstveröffentlichers |
| Zusammenfassung: | Independent Component Analysis (ICA) has recently become an important tool for modelling and understanding empirical datasets. It is a method of separating out independent sources from linearly mixed data, and belongs to the class of general linear models. ICA provides a better decomposition than other well-known models such as principal component analysis. This self-contained book contains a structured series of edited papers by leading researchers in the field, including an extensive introduction to ICA. The major theoretical bases are reviewed from a modern perspective, current developments are surveyed and many case studies of applications are described in detail. The latter include biomedical examples, signal and image denoising and mobile communications. ICA is discussed in the framework of general linear models, but also in comparison with other paradigms such as neural network and graphical modelling methods. The book is ideal for researchers and graduate students in the field |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (xii, 338 pages) |
| ISBN: | 9780511624148 |
| DOI: | 10.1017/CBO9780511624148 |
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| 505 | 8 | |a Fast ICA by a fixed-point algorithm that maximizes non-gaussianity / Aapo Hyvarinen -- ICA, graphical models and variational methods / H. Attias -- Nonlinear ICA / J. Karhunen -- Separation of non-stationary natural signals / Lucas C. Parra & Clay D. Spence -- Separation of non-stationary sources / Jean-Francois Cardoso & Dinh-Tuan Pham -- Blind source separation by sparse decomposition in a signal dictionary / M. Zibulevsky, B.A. Pearlmutter, P. Bofill & P. Kisilev -- Ensemble learning for blind source separation / J.W. Miskin & D.J.C. MacKay -- Image processing methods using ICA mixture models / T.-W. Lee & M.S. Lewicki -- Latent class and trait models for data classification and visualisation / M.A. Girolami -- Particle filters for non-stationary ICA / R.M. Everson & S.J. Roberts -- ICA / W.D. Penny, S.J. Roberts & R.M. Everson | |
| 520 | |a Independent Component Analysis (ICA) has recently become an important tool for modelling and understanding empirical datasets. It is a method of separating out independent sources from linearly mixed data, and belongs to the class of general linear models. ICA provides a better decomposition than other well-known models such as principal component analysis. This self-contained book contains a structured series of edited papers by leading researchers in the field, including an extensive introduction to ICA. The major theoretical bases are reviewed from a modern perspective, current developments are surveyed and many case studies of applications are described in detail. The latter include biomedical examples, signal and image denoising and mobile communications. ICA is discussed in the framework of general linear models, but also in comparison with other paradigms such as neural network and graphical modelling methods. The book is ideal for researchers and graduate students in the field | ||
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| 650 | 4 | |a Independent component analysis | |
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Datensatz im Suchindex
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| author2 | Roberts, Stephen A. 1946- Everson, Richard 1961- |
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| author_GND | (DE-588)172333792 |
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| contents | Fast ICA by a fixed-point algorithm that maximizes non-gaussianity / Aapo Hyvarinen -- ICA, graphical models and variational methods / H. Attias -- Nonlinear ICA / J. Karhunen -- Separation of non-stationary natural signals / Lucas C. Parra & Clay D. Spence -- Separation of non-stationary sources / Jean-Francois Cardoso & Dinh-Tuan Pham -- Blind source separation by sparse decomposition in a signal dictionary / M. Zibulevsky, B.A. Pearlmutter, P. Bofill & P. Kisilev -- Ensemble learning for blind source separation / J.W. Miskin & D.J.C. MacKay -- Image processing methods using ICA mixture models / T.-W. Lee & M.S. Lewicki -- Latent class and trait models for data classification and visualisation / M.A. Girolami -- Particle filters for non-stationary ICA / R.M. Everson & S.J. Roberts -- ICA / W.D. Penny, S.J. Roberts & R.M. Everson |
| ctrlnum | (ZDB-20-CBO)CR9780511624148 (OCoLC)967603731 (DE-599)BVBBV043944343 |
| dewey-full | 006.3/2 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/2 |
| dewey-search | 006.3/2 |
| dewey-sort | 16.3 12 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Psychologie Mathematik |
| doi_str_mv | 10.1017/CBO9780511624148 |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:39:21Z |
| institution | BVB |
| isbn | 9780511624148 |
| language | English |
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| spelling | Independent component analysis principles and practice edited by Stephen Roberts, Richard Everson Cambridge Cambridge University Press 2014 1 online resource (xii, 338 pages) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) Fast ICA by a fixed-point algorithm that maximizes non-gaussianity / Aapo Hyvarinen -- ICA, graphical models and variational methods / H. Attias -- Nonlinear ICA / J. Karhunen -- Separation of non-stationary natural signals / Lucas C. Parra & Clay D. Spence -- Separation of non-stationary sources / Jean-Francois Cardoso & Dinh-Tuan Pham -- Blind source separation by sparse decomposition in a signal dictionary / M. Zibulevsky, B.A. Pearlmutter, P. Bofill & P. Kisilev -- Ensemble learning for blind source separation / J.W. Miskin & D.J.C. MacKay -- Image processing methods using ICA mixture models / T.-W. Lee & M.S. Lewicki -- Latent class and trait models for data classification and visualisation / M.A. Girolami -- Particle filters for non-stationary ICA / R.M. Everson & S.J. Roberts -- ICA / W.D. Penny, S.J. Roberts & R.M. Everson Independent Component Analysis (ICA) has recently become an important tool for modelling and understanding empirical datasets. It is a method of separating out independent sources from linearly mixed data, and belongs to the class of general linear models. ICA provides a better decomposition than other well-known models such as principal component analysis. This self-contained book contains a structured series of edited papers by leading researchers in the field, including an extensive introduction to ICA. The major theoretical bases are reviewed from a modern perspective, current developments are surveyed and many case studies of applications are described in detail. The latter include biomedical examples, signal and image denoising and mobile communications. ICA is discussed in the framework of general linear models, but also in comparison with other paradigms such as neural network and graphical modelling methods. The book is ideal for researchers and graduate students in the field Neural networks (Computer science) Independent component analysis Komponentenanalyse (DE-588)4133251-9 gnd rswk-swf Signalverarbeitung (DE-588)4054947-1 gnd rswk-swf Komponentenanalyse (DE-588)4133251-9 s Signalverarbeitung (DE-588)4054947-1 s 1\p DE-604 Roberts, Stephen A. 1946- (DE-588)172333792 edt Everson, Richard 1961- edt Erscheint auch als Druck-Ausgabe, Hardcover 978-0-521-79298-1 https://doi.org/10.1017/CBO9780511624148 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Independent component analysis principles and practice Fast ICA by a fixed-point algorithm that maximizes non-gaussianity / Aapo Hyvarinen -- ICA, graphical models and variational methods / H. Attias -- Nonlinear ICA / J. Karhunen -- Separation of non-stationary natural signals / Lucas C. Parra & Clay D. Spence -- Separation of non-stationary sources / Jean-Francois Cardoso & Dinh-Tuan Pham -- Blind source separation by sparse decomposition in a signal dictionary / M. Zibulevsky, B.A. Pearlmutter, P. Bofill & P. Kisilev -- Ensemble learning for blind source separation / J.W. Miskin & D.J.C. MacKay -- Image processing methods using ICA mixture models / T.-W. Lee & M.S. Lewicki -- Latent class and trait models for data classification and visualisation / M.A. Girolami -- Particle filters for non-stationary ICA / R.M. Everson & S.J. Roberts -- ICA / W.D. Penny, S.J. Roberts & R.M. Everson Neural networks (Computer science) Independent component analysis Komponentenanalyse (DE-588)4133251-9 gnd Signalverarbeitung (DE-588)4054947-1 gnd |
| subject_GND | (DE-588)4133251-9 (DE-588)4054947-1 |
| title | Independent component analysis principles and practice |
| title_auth | Independent component analysis principles and practice |
| title_exact_search | Independent component analysis principles and practice |
| title_full | Independent component analysis principles and practice edited by Stephen Roberts, Richard Everson |
| title_fullStr | Independent component analysis principles and practice edited by Stephen Roberts, Richard Everson |
| title_full_unstemmed | Independent component analysis principles and practice edited by Stephen Roberts, Richard Everson |
| title_short | Independent component analysis |
| title_sort | independent component analysis principles and practice |
| title_sub | principles and practice |
| topic | Neural networks (Computer science) Independent component analysis Komponentenanalyse (DE-588)4133251-9 gnd Signalverarbeitung (DE-588)4054947-1 gnd |
| topic_facet | Neural networks (Computer science) Independent component analysis Komponentenanalyse Signalverarbeitung |
| url | https://doi.org/10.1017/CBO9780511624148 |
| work_keys_str_mv | AT robertsstephena independentcomponentanalysisprinciplesandpractice AT eversonrichard independentcomponentanalysisprinciplesandpractice |