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Advances in independent component analysis and learning machines /
In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have dev...
| Call Number: | Libro Electrónico |
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| Other Authors: | , , , , |
| Format: | Electronic eBook |
| Language: | Inglés |
| Published: |
London, UK :
Academic Press,
2015.
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| Subjects: | |
| Online Access: | Texto completo |
Table of Contents:
- 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.