Advances in independent component analysis and learning machines:
Gespeichert in:
| Weitere Verfasser: | , , , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam
Elsevier Academic Press
[2015]
|
| Schlagworte: | |
| Online-Zugang: | FAW01 TUM01 UER01 URL des Erstveröffentlichers |
| Beschreibung: | Includes index Front Cover; Advances in Independent Component Analysis and Learning Machines; Copyright; Contents; Preface; About the Editors; List of Contributors; Introduction; A Student and a Co-Worker; Prof. Simon Haykin; Prof. Jos e Pr incipe; Prof. T ulay Adali; Prof. Lu is Borges de Almeida; Prof. Christian Jutten; Prof. Mark Plumbley; Prof. Klaus-Robert M uller and Dr. Andreas Ziehe; Chapter abstracts; Chapter 1; The initial convergence rate of the FastICA algorithm: The ''One-Third Rule''; Scott C. Douglas; Chapter 2; Improved variants of the FastICA algorithm; Zbynvek Koldovsk y and Petr Tichavsk y Chapter 3A unified probabilistic model for independent and principal component analysis; Aapo Hyv arinen; Chapter 4; Riemannian optimization in complex-valued ICA; Visa Koivunen and Traian Abrudan; Chapter 5; Nonadditive optimization; Zhirong Yang and Irwin King; Chapter 6; Image denoising, local factor analysis, Bayesian Ying-Yang harmony learning; Guangyong Chen, Fengyuan Zhu, Pheng Ann Heng and Lei Xu; Chapter 7; Unsupervised deep learning: A short review; Juha Karhunen, Tapani Raiko and KyungHyun Cho; Chapter 8; From neural PCA to deep unsupervised learning; Harri Valpola; Chapter 9 Two decades of local binary patterns: A surveyMatti Pietik ainen and Guoying Zhao; Chapter 10; Subspace approach in spectral color science; Jussi Parkkinen, Hannu Laamanen and Markku Hauta-Kasari; Chapter 11; From pattern recognition methods to machine vision applications; Heikki K alvi ainen; Chapter 12; Advances in visual concept detection: Ten years of TRECVID; Ville Viitaniemi, Mats Sj oberg, Markus Koskela, Satoru Ishikawa and Jorma Laaksonen; Chapter 13; On the applicability of latent variable modeling to research system data; Ella Bingham and Heikki Mannila; Part I: Methods Chapter 1: The initial convergence rate of the FastICA algorithm: The ''One-Third Rule''1.1 Introduction; 1.2 Statistical analysis of the FastICA algorithm; 1.3 Stationary point analysis of the FastICA algorithm; 1.4 Initial convergence of the FastICA algorithm for two-source mixtures; 1.4.1 Overview of results; 1.4.2 Preliminaries; 1.4.3 Equal-kurtosis sources case; 1.4.3.1 A bound on the average ICI; 1.4.3.2 The probability density function of the ICI; 1.4.3.3 The average value of the ICI; 1.4.4 Arbitrary-kurtosis sources case 1.5 Initial convergence of the FastICA algorithm for three or more source mixtures1.5.1 Overview of results; 1.5.2 Preliminaries; 1.5.3 Three-source case; 1.5.4 Four-source case; 1.5.5 General m-source case; 1.5.6 Equal-kurtosis m-source case using order statistics; 1.6 Numerical evaluations; 1.7 Conclusion; Appendix; Proof of Theorem 1; Proof of Theorems 2 and 3; Proof of Theorem 4; Proofs of Theorem 5 and Associated Corollaries; Proof of Theorem 6; Proof of Theorem 7; Proof of Theorem 8; Proof of Theorem 9; Proof of Theorem 10; Proof of Theorem 11; Proof of Theorem 12; Acknowledgments |
| Beschreibung: | 1 online resource |
| ISBN: | 9780128028070 |
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| 245 | 1 | 0 | |a Advances in independent component analysis and learning machines |c edited by Ella Bingham, Samuel Kaski, Jorma Laaksonen, Jouko Lampinen |
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| 500 | |a Includes index | ||
| 500 | |a Front Cover; Advances in Independent Component Analysis and Learning Machines; Copyright; Contents; Preface; About the Editors; List of Contributors; Introduction; A Student and a Co-Worker; Prof. Simon Haykin; Prof. Jos e Pr incipe; Prof. T ulay Adali; Prof. Lu is Borges de Almeida; Prof. Christian Jutten; Prof. Mark Plumbley; Prof. Klaus-Robert M uller and Dr. Andreas Ziehe; Chapter abstracts; Chapter 1; The initial convergence rate of the FastICA algorithm: The ''One-Third Rule''; Scott C. Douglas; Chapter 2; Improved variants of the FastICA algorithm; Zbynvek Koldovsk y and Petr Tichavsk y | ||
| 500 | |a Chapter 3A unified probabilistic model for independent and principal component analysis; Aapo Hyv arinen; Chapter 4; Riemannian optimization in complex-valued ICA; Visa Koivunen and Traian Abrudan; Chapter 5; Nonadditive optimization; Zhirong Yang and Irwin King; Chapter 6; Image denoising, local factor analysis, Bayesian Ying-Yang harmony learning; Guangyong Chen, Fengyuan Zhu, Pheng Ann Heng and Lei Xu; Chapter 7; Unsupervised deep learning: A short review; Juha Karhunen, Tapani Raiko and KyungHyun Cho; Chapter 8; From neural PCA to deep unsupervised learning; Harri Valpola; Chapter 9 | ||
| 500 | |a Two decades of local binary patterns: A surveyMatti Pietik ainen and Guoying Zhao; Chapter 10; Subspace approach in spectral color science; Jussi Parkkinen, Hannu Laamanen and Markku Hauta-Kasari; Chapter 11; From pattern recognition methods to machine vision applications; Heikki K alvi ainen; Chapter 12; Advances in visual concept detection: Ten years of TRECVID; Ville Viitaniemi, Mats Sj oberg, Markus Koskela, Satoru Ishikawa and Jorma Laaksonen; Chapter 13; On the applicability of latent variable modeling to research system data; Ella Bingham and Heikki Mannila; Part I: Methods | ||
| 500 | |a Chapter 1: The initial convergence rate of the FastICA algorithm: The ''One-Third Rule''1.1 Introduction; 1.2 Statistical analysis of the FastICA algorithm; 1.3 Stationary point analysis of the FastICA algorithm; 1.4 Initial convergence of the FastICA algorithm for two-source mixtures; 1.4.1 Overview of results; 1.4.2 Preliminaries; 1.4.3 Equal-kurtosis sources case; 1.4.3.1 A bound on the average ICI; 1.4.3.2 The probability density function of the ICI; 1.4.3.3 The average value of the ICI; 1.4.4 Arbitrary-kurtosis sources case | ||
| 500 | |a 1.5 Initial convergence of the FastICA algorithm for three or more source mixtures1.5.1 Overview of results; 1.5.2 Preliminaries; 1.5.3 Three-source case; 1.5.4 Four-source case; 1.5.5 General m-source case; 1.5.6 Equal-kurtosis m-source case using order statistics; 1.6 Numerical evaluations; 1.7 Conclusion; Appendix; Proof of Theorem 1; Proof of Theorems 2 and 3; Proof of Theorem 4; Proofs of Theorem 5 and Associated Corollaries; Proof of Theorem 6; Proof of Theorem 7; Proof of Theorem 8; Proof of Theorem 9; Proof of Theorem 10; Proof of Theorem 11; Proof of Theorem 12; Acknowledgments | ||
| 650 | 4 | |a Pattern recognition systems | |
| 650 | 4 | |a Image processing / Digital techniques | |
| 650 | 4 | |a Machine learning | |
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| 700 | 1 | |a Kaski, Samuel |4 edt | |
| 700 | 1 | |a Laaksonen, Jorma |4 edt | |
| 700 | 1 | |a Lampinen, Jouko |4 edt | |
| 700 | 1 | |a Oja, Erkki |e Sonstige |4 oth | |
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Datensatz im Suchindex
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| id | DE-604.BV042940835 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:13:19Z |
| institution | BVB |
| isbn | 9780128028070 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028367326 |
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| publishDate | 2015 |
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| publisher | Elsevier Academic Press |
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| spelling | Advances in independent component analysis and learning machines edited by Ella Bingham, Samuel Kaski, Jorma Laaksonen, Jouko Lampinen Amsterdam Elsevier Academic Press [2015] © 2015 1 online resource txt rdacontent c rdamedia cr rdacarrier Includes index Front Cover; Advances in Independent Component Analysis and Learning Machines; Copyright; Contents; Preface; About the Editors; List of Contributors; Introduction; A Student and a Co-Worker; Prof. Simon Haykin; Prof. Jos e Pr incipe; Prof. T ulay Adali; Prof. Lu is Borges de Almeida; Prof. Christian Jutten; Prof. Mark Plumbley; Prof. Klaus-Robert M uller and Dr. Andreas Ziehe; Chapter abstracts; Chapter 1; The initial convergence rate of the FastICA algorithm: The ''One-Third Rule''; Scott C. Douglas; Chapter 2; Improved variants of the FastICA algorithm; Zbynvek Koldovsk y and Petr Tichavsk y Chapter 3A unified probabilistic model for independent and principal component analysis; Aapo Hyv arinen; Chapter 4; Riemannian optimization in complex-valued ICA; Visa Koivunen and Traian Abrudan; Chapter 5; Nonadditive optimization; Zhirong Yang and Irwin King; Chapter 6; Image denoising, local factor analysis, Bayesian Ying-Yang harmony learning; Guangyong Chen, Fengyuan Zhu, Pheng Ann Heng and Lei Xu; Chapter 7; Unsupervised deep learning: A short review; Juha Karhunen, Tapani Raiko and KyungHyun Cho; Chapter 8; From neural PCA to deep unsupervised learning; Harri Valpola; Chapter 9 Two decades of local binary patterns: A surveyMatti Pietik ainen and Guoying Zhao; Chapter 10; Subspace approach in spectral color science; Jussi Parkkinen, Hannu Laamanen and Markku Hauta-Kasari; Chapter 11; From pattern recognition methods to machine vision applications; Heikki K alvi ainen; Chapter 12; Advances in visual concept detection: Ten years of TRECVID; Ville Viitaniemi, Mats Sj oberg, Markus Koskela, Satoru Ishikawa and Jorma Laaksonen; Chapter 13; On the applicability of latent variable modeling to research system data; Ella Bingham and Heikki Mannila; Part I: Methods Chapter 1: The initial convergence rate of the FastICA algorithm: The ''One-Third Rule''1.1 Introduction; 1.2 Statistical analysis of the FastICA algorithm; 1.3 Stationary point analysis of the FastICA algorithm; 1.4 Initial convergence of the FastICA algorithm for two-source mixtures; 1.4.1 Overview of results; 1.4.2 Preliminaries; 1.4.3 Equal-kurtosis sources case; 1.4.3.1 A bound on the average ICI; 1.4.3.2 The probability density function of the ICI; 1.4.3.3 The average value of the ICI; 1.4.4 Arbitrary-kurtosis sources case 1.5 Initial convergence of the FastICA algorithm for three or more source mixtures1.5.1 Overview of results; 1.5.2 Preliminaries; 1.5.3 Three-source case; 1.5.4 Four-source case; 1.5.5 General m-source case; 1.5.6 Equal-kurtosis m-source case using order statistics; 1.6 Numerical evaluations; 1.7 Conclusion; Appendix; Proof of Theorem 1; Proof of Theorems 2 and 3; Proof of Theorem 4; Proofs of Theorem 5 and Associated Corollaries; Proof of Theorem 6; Proof of Theorem 7; Proof of Theorem 8; Proof of Theorem 9; Proof of Theorem 10; Proof of Theorem 11; Proof of Theorem 12; Acknowledgments Pattern recognition systems Image processing / Digital techniques Machine learning Unabhängige Komponentenanalyse (DE-588)4812492-8 gnd rswk-swf Unabhängige Komponentenanalyse (DE-588)4812492-8 s DE-604 Bingham, Ella (DE-588)1074261569 edt Kaski, Samuel edt Laaksonen, Jorma edt Lampinen, Jouko edt Oja, Erkki Sonstige oth Erscheint auch als Druck-Ausgabe 978-0-12-802806-3 http://www.sciencedirect.com/science/book/9780128028063 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Advances in independent component analysis and learning machines Pattern recognition systems Image processing / Digital techniques Machine learning Unabhängige Komponentenanalyse (DE-588)4812492-8 gnd |
| subject_GND | (DE-588)4812492-8 |
| title | Advances in independent component analysis and learning machines |
| title_auth | Advances in independent component analysis and learning machines |
| title_exact_search | Advances in independent component analysis and learning machines |
| title_full | Advances in independent component analysis and learning machines edited by Ella Bingham, Samuel Kaski, Jorma Laaksonen, Jouko Lampinen |
| title_fullStr | Advances in independent component analysis and learning machines edited by Ella Bingham, Samuel Kaski, Jorma Laaksonen, Jouko Lampinen |
| title_full_unstemmed | Advances in independent component analysis and learning machines edited by Ella Bingham, Samuel Kaski, Jorma Laaksonen, Jouko Lampinen |
| title_short | Advances in independent component analysis and learning machines |
| title_sort | advances in independent component analysis and learning machines |
| topic | Pattern recognition systems Image processing / Digital techniques Machine learning Unabhängige Komponentenanalyse (DE-588)4812492-8 gnd |
| topic_facet | Pattern recognition systems Image processing / Digital techniques Machine learning Unabhängige Komponentenanalyse |
| url | http://www.sciencedirect.com/science/book/9780128028063 |
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