Independent component analysis-(E-BOOK) [Elektronisk resurs] Aapo Hyv䲩nen, Juha Karhunen, Erkki Oja

By: Hyv䲩nen, AapoContributor(s): Karhunen, Juha | Oja, ErkkiSeries: Adaptive and learning systems for signal processing, communications and controlPublisher: New York : Wiley, 2002Description: PDF-fil (xxi, 481 s.)ISBN: 0-471-22131-7; 978-0-471-22131-9Subject(s): Principal components analysisDDC classification: 519.5354 Online resources: Click here to access online
Contents:
Introduction (Page-1) Part I MATHEMATICAL PRELIMINARIES, 2 Random Vectors and Independence (Page-15) 3 Gradients and Optimization Methods (Page-57) 4 Estimation Theory (Page-77) 5 Information Theory (page-105) 6 Principal Component Analysis and Whitening (page-125), Part II BASIC INDEPENDENT COMPONENT ANALYSIS, 7 What is Independent Component Analysis? (Page-147) 8 ICA by Maximization of Nongaussianity (page-165) 9 ICA by Maximum Likelihood Estimation (page-203) 10 ICA by Minimization of Mutual Information (pag-221) 11 ICA by Tensorial Methods (page-229) 12 ICA by Nonlinear Decorrelation and Nonlinear PCA (page-239) 13 Practical Considerations (page-263) 14 Overview and Comparison of Basic ICA Methods(Page-273) Part III EXTENSIONS AND RELATED METHODS, 15 Noisy ICA (page-293) 16 ICA with Overcomplete Bases (Page-305) 17 Nonlinear ICA (page-315) 18 Methods using Time Structure (page-341) 19 Convolutive Mixtures and Blind Deconvolution (page-355) 20 Other Extensions (page-371) Part IV APPLICATIONS OF ICA, 21 Feature Extraction by ICA (page-391) 22 Brain Imaging Applications (page-407) 23 Telecommunications (page-417) 24 Other Applications (Page-441)
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Item type Current location Home library Collection Call number URL Status Date due Barcode Item holds
Book Book Military College of Signals (MCS)
Military College of Signals (MCS)
NFIC 519.5354 HYV (Browse shelf) Link to resource Available MCSEB-741
Total holds: 0

Introduction (Page-1) Part I MATHEMATICAL PRELIMINARIES, 2 Random Vectors and Independence (Page-15) 3 Gradients and Optimization Methods (Page-57) 4 Estimation Theory (Page-77) 5 Information Theory (page-105) 6 Principal Component Analysis and Whitening (page-125), Part II BASIC INDEPENDENT COMPONENT ANALYSIS, 7 What is Independent Component Analysis? (Page-147) 8 ICA by Maximization of Nongaussianity (page-165) 9 ICA by Maximum Likelihood Estimation (page-203) 10 ICA by Minimization of Mutual Information (pag-221) 11 ICA by Tensorial Methods (page-229) 12 ICA by Nonlinear Decorrelation and Nonlinear PCA (page-239) 13 Practical Considerations (page-263) 14 Overview and Comparison of Basic ICA Methods(Page-273) Part III EXTENSIONS AND RELATED METHODS, 15 Noisy ICA (page-293) 16 ICA with Overcomplete Bases (Page-305) 17 Nonlinear ICA (page-315) 18 Methods using Time Structure (page-341) 19 Convolutive Mixtures and Blind Deconvolution (page-355) 20 Other Extensions (page-371) Part IV APPLICATIONS OF ICA, 21 Feature Extraction by ICA (page-391) 22 Brain Imaging Applications (page-407) 23 Telecommunications (page-417) 24 Other Applications (Page-441)

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