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Advances in independent com...
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 developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithmUnsupervised deep learning Machine vision and image retrieval A review of developments in the t.
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monografia Rebiun17770378 https://catalogo.rebiun.org/rebiun/record/Rebiun17770378 m o d cr cnu|||unuuu 150519s2015 enk o 011 0 eng d 9780128028070 0128028076 9780128028063 UPVA 997028556103706 NT. eng. pn. NT. OPELS. NT. CDX. IDEBK. E7B. EBLCP. YDXCP. DEBSZ. UNAV 006.4 23 Advances in independent component analysis and learning machines Recurso electrónico] edited by Ella Bingham, Samuel Kaski, Jorma Laaksonen, Jouko Lampinen London, UK Academic Press 2015 London, UK London, UK Academic Press 1 recurso electrónico 1 recurso electrónico Science Direct e-books Incluye índices 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. José Príncipe; Prof. Tülay Adali; Prof. Luís Borges de Almeida; Prof. Christian Jutten; Prof. Mark Plumbley; Prof. Klaus-Robert Müller and Dr. Andreas Ziehe; Chapter abstracts; Chapter 1; The initial convergence rate of the FastICA algorithm: The Òne-Third Rule''; Scott C. Douglas; Chapter 2; Improved variants of the FastICA algorithm; Zbynvek Koldovsky and Petr Tichavsky Chapter 3A unified probabilistic model for independent and principal component analysis; Aapo Hyvärinen; 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 Pietikäinen 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 Kälviäinen; Chapter 12; Advances in visual concept detection: Ten years of TRECVID; Ville Viitaniemi, Mats Sjöberg, 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 Òne-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 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 developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithmUnsupervised deep learning Machine vision and image retrieval A review of developments in the t. Forma de acceso: World Wide Web Bingham, Ella Kaski, Samuel Laaksonen, Jorma Lampinen, Jouko Oja, Erkki