{"product_id":"blind-source-separation-isbn-9781118679845","title":"Blind Source Separation","description":"\u003cp\u003e\u003cb\u003e\u003ci\u003eA systematic exploration of both classic and contemporary algorithms in blind source separation with practical case studies   \u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe book presents an overview of Blind Source Separation, a relatively new signal processing method.  Due to the multidisciplinary nature of the subject, the book has been written so as to appeal to an audience from very different backgrounds. Basic mathematical skills (e.g. on matrix algebra and foundations of probability theory) are essential in order to understand the algorithms, although the book is written in an introductory, accessible style.\u003c\/p\u003e \u003cp\u003eThis book offers a general overview of the basics of Blind Source Separation, important solutions and algorithms, and in-depth coverage of applications in image feature extraction, remote sensing image fusion, mixed-pixel decomposition of SAR images, image object recognition fMRI medical image processing, geochemical and geophysical data mining, mineral resources prediction and geoanomalies information recognition. Firstly, the background and theory basics of blind source separation are introduced, which provides the foundation for the following work. Matrix operation, foundations of probability theory and information theory basics are included here. There follows the fundamental mathematical model and fairly new but relatively established blind source separation algorithms, such as Independent Component Analysis (ICA) and its improved algorithms (Fast ICA, Maximum Likelihood ICA, Overcomplete ICA, Kernel ICA, Flexible ICA, Non-negative ICA, Constrained ICA, Optimised ICA). The last part of the book considers the very recent algorithms in BSS e.g. Sparse Component Analysis (SCA) and Non-negative Matrix Factorization (NMF). Meanwhile, in-depth cases are presented for each algorithm in order to help the reader understand the algorithm and its application field.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eA systematic exploration of both classic and contemporary algorithms in blind source separation with practical case studies\u003c\/li\u003e \u003cli\u003ePresents new improved algorithms aimed at different applications, such as image feature extraction, remote sensing image fusion, mixed-pixel decomposition of SAR images, image object recognition, and MRI medical image processing\u003c\/li\u003e \u003cli\u003eWith applications in geochemical and geophysical data mining, mineral resources prediction and geoanomalies information recognition\u003c\/li\u003e \u003cli\u003eWritten by an expert team with accredited innovations in blind source separation and its applications in natural science\u003c\/li\u003e \u003cli\u003eAccompanying website includes a software system providing codes for most of the algorithms mentioned in the book, enhancing the learning experience\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eEssential reading for postgraduate students and researchers engaged in the area of signal processing, data mining, image processing and recognition, information, geosciences, life sciences.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAbout the Authors xiii\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003ePreface xv\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAcknowledgements xvii\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eGlossary xix\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Overview of Blind Source Separation 1\u003c\/p\u003e \u003cp\u003e1.2 History of BSS 4\u003c\/p\u003e \u003cp\u003e1.3 Applications of BSS 8\u003c\/p\u003e \u003cp\u003e1.4 Contents of the Book 10\u003c\/p\u003e \u003cp\u003eReferences 11\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I BASIC THEORY OF BSS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Mathematical Foundation of Blind Source Separation 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Matrix Analysis and Computing 19\u003c\/p\u003e \u003cp\u003e2.2 Foundation of Probability Theory for Higher-Order Statistics 28\u003c\/p\u003e \u003cp\u003e2.3 Basic Concepts of Information Theory 33\u003c\/p\u003e \u003cp\u003e2.4 Distance Measure 37\u003c\/p\u003e \u003cp\u003e2.5 Solvability of the Signal Blind Source Separation Problem 40\u003c\/p\u003e \u003cp\u003eFurther Reading 41\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 General Model and Classical Algorithm for BSS 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Mathematical Model 43\u003c\/p\u003e \u003cp\u003e3.2 BSS Algorithm 46\u003c\/p\u003e \u003cp\u003eReferences 51\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Evaluation Criteria for the BSS Algorithm 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Evaluation Criteria for Objective Functions 53\u003c\/p\u003e \u003cp\u003e4.2 Evaluation Criteria for Correlations 57\u003c\/p\u003e \u003cp\u003e4.3 Evaluation Criteria for Signal-to-Noise Ratio 57\u003c\/p\u003e \u003cp\u003eReferences 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II INDEPENDENT COMPONENT ANALYSIS AND APPLICATIONS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Independent Component Analysis 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 History of ICA 61\u003c\/p\u003e \u003cp\u003e5.2 Principle of ICA 65\u003c\/p\u003e \u003cp\u003e5.3 Chapter Summary 82\u003c\/p\u003e \u003cp\u003eReferences 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Fast Independent Component Analysis and Its Application 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Overview 85\u003c\/p\u003e \u003cp\u003e6.2 FastICA Algorithm 89\u003c\/p\u003e \u003cp\u003e6.3 Application and Analysis 92\u003c\/p\u003e \u003cp\u003e6.4 Conclusion 118\u003c\/p\u003e \u003cp\u003eReferences 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Maximum Likelihood Independent Component Analysis and Its Application 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Overview 121\u003c\/p\u003e \u003cp\u003e7.2 Algorithms for Maximum Likelihood Estimation 123\u003c\/p\u003e \u003cp\u003e7.3 Application and Analysis 130\u003c\/p\u003e \u003cp\u003e7.4 Chapter Summary 133\u003c\/p\u003e \u003cp\u003eReferences 133\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Overcomplete Independent Component Analysis Algorithms and Applications 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Overcomplete ICA Algorithms 135\u003c\/p\u003e \u003cp\u003e8.2 Applications and Analysis 139\u003c\/p\u003e \u003cp\u003e8.3 Chapter Summary 143\u003c\/p\u003e \u003cp\u003eReferences 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Kernel Independent Component Analysis 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 KICA Algorithm 145\u003c\/p\u003e \u003cp\u003e9.2 Application and Analysis 147\u003c\/p\u003e \u003cp\u003e9.3 Concluding Remarks 149\u003c\/p\u003e \u003cp\u003eReferences 152\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Natural Gradient Flexible ICA Algorithm and Its Application 153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Natural Gradient Flexible ICA Algorithm 153\u003c\/p\u003e \u003cp\u003e10.2 Application and Analysis 156\u003c\/p\u003e \u003cp\u003e10.3 Chapter Summary 166\u003c\/p\u003e \u003cp\u003eReferences 166\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Non-negative Independent Component Analysis and Its Application 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Non-negative Independent Component Analysis 168\u003c\/p\u003e \u003cp\u003e11.2 Application and Analysis 169\u003c\/p\u003e \u003cp\u003e11.3 Chapter Summary 182\u003c\/p\u003e \u003cp\u003eReferences 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Constraint Independent Component Analysis Algorithms and Applications 183\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Overview 183\u003c\/p\u003e \u003cp\u003e12.2 CICA Algorithm 185\u003c\/p\u003e \u003cp\u003e12.3 Application and Analysis 189\u003c\/p\u003e \u003cp\u003e12.4 Chapter Summary 196\u003c\/p\u003e \u003cp\u003eReferences 196\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Optimized Independent Component Analysis Algorithms and Applications 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Overview 199\u003c\/p\u003e \u003cp\u003e13.2 Optimized ICA Algorithm 200\u003c\/p\u003e \u003cp\u003e13.3 Application and Analysis 205\u003c\/p\u003e \u003cp\u003e13.4 Chapter Summary 221\u003c\/p\u003e \u003cp\u003eReferences 222\u003cbr\u003e\u003cbr\u003e\u003cb\u003e14 Supervised Learning Independent Component Analysis Algorithms and Applications 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Overview 225\u003c\/p\u003e \u003cp\u003e14.2 Mathematical Model 226\u003c\/p\u003e \u003cp\u003e14.3 Principles of SL-ICA 227\u003c\/p\u003e \u003cp\u003e14.4 SL-ICA Implementation Process 230\u003c\/p\u003e \u003cp\u003e14.5 The Experiment 230\u003c\/p\u003e \u003cp\u003e14.6 Chapter Summary 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix 14.A Polarization Channel SAR Images of Beijing and the \u003c\/b\u003e\u003cb\u003eDecomposition Results using SL-ICA 239\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences 242\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III ADVANCES AND APPLICATIONS OF BSS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Non-negative Matrix Factorization Algorithms and Applications 247\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 247\u003c\/p\u003e \u003cp\u003e15.2 NMF Algorithms 251\u003c\/p\u003e \u003cp\u003e15.3 Applications and Analysis 276\u003c\/p\u003e \u003cp\u003e15.4 Chapter Summary 309\u003c\/p\u003e \u003cp\u003eReferences 310\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Sparse Component Analysis and Applications 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Overview 314\u003c\/p\u003e \u003cp\u003e16.2 Linear Clustering SCA (LC-SCA) 321\u003c\/p\u003e \u003cp\u003e16.3 Plane Clustering SCA (PC-SCA) 332\u003c\/p\u003e \u003cp\u003e16.4 Over-Complete SCA Based on Plane Clustering (PCO-SCA) 336\u003c\/p\u003e \u003cp\u003e16.5 Blind Image Separation Based on Wavelets and SCA (WL-SCA) 340\u003c\/p\u003e \u003cp\u003e16.6 New BSS Algorithm Based on Feedback SCA 343\u003c\/p\u003e \u003cp\u003e16.7 Remote Sensing Image Classification Based on SCA 351\u003c\/p\u003e \u003cp\u003e16.8 Chapter Summary 357\u003c\/p\u003e \u003cp\u003e\u003ci\u003eReferences 357\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eIndex 361\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eXianchuan Yu\u003c\/b\u003e, Beijing Normal University, P. R. China\u003c\/p\u003e \u003cp\u003e\u003cb\u003eDan Hu\u003c\/b\u003e, Beijing Normal University, P. R. China\u003c\/p\u003e \u003cp\u003e\u003cb\u003eJindong Xu\u003c\/b\u003e, Beijing Normal University, P. R. China\u003c\/p\u003e  \u003cp\u003eBlind source separation is a relatively new signal processing method combining artificial neural networks, statistical information processing and information theory. It has tremendous potential in applications such as processing of speech, image, and biomedical signals. The technique excels in signal extraction, enhancement, denoising, model reduction and classification problems.\u003c\/p\u003e \u003cp\u003eThis book provides an overview of the basics of blind source separation along with important solutions and algorithms. Applications are also covered in-depth, including image feature extraction, remote sensing image fusion, mixed-pixel decomposition of SAR images, image object recognition, fMRI medical image processing, geochemical and geophysical data mining, mineral resources prediction and geo-anomalies information recognition. Given the multidisciplinary nature of the subject the book has been written in an accessible style so as to appeal to readers from very different backgrounds.\u003c\/p\u003e \u003cp\u003e• Gives a systematic exploration of both classic and contemporary algorithms in blind source separation with practical case  studies\u003c\/p\u003e \u003cp\u003e• Written by an expert team with innovations in blind source separation and its applications in natural science\u003c\/p\u003e \u003cp\u003e• Codes for most of the algorithms mentioned in the book available from the author\u003c\/p\u003e \u003cp\u003e This book is aimed at graduate students and researchers engaged in the areas of\u003cbr\u003e signal processing, data mining, image processing and recognition, computational\u003cbr\u003e geosciences, computational life sciences, and other field sciences.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988847345893,"sku":"NP9781118679845","price":209.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118679845.jpg?v=1761781758","url":"https:\/\/k12savings.com\/products\/blind-source-separation-isbn-9781118679845","provider":"K12savings","version":"1.0","type":"link"}