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Independent Component Analysis(2004) 요약정보 및 구매

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지은이 Stone
발행년도 2004-01-01
판수 1판
페이지 200
ISBN 9780262693158
도서상태 구매가능
판매가격 42,840원
포인트 0점
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  • Independent Component Analysis(2004)
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관련상품

  • Independent component analysis (ICA) is becoming an increasingly important tool for analyzing large data sets. In essence, ICA separates an observed set of signal mixtures into a set of statistically independent component signals, or source signals. In so doing, this powerful method can extract the relatively small amount of useful information typically found in large data sets. The applications for ICA range from speech processing, brain imaging, and electrical brain signals to telecommunications and stock predictions.In Independent Component Analysis, Jim Stone presents the essentials of ICA and related techniques (projection pursuit and complexity pursuit) in a tutorial style, using intuitive examples described in simple geometric terms. The treatment fills the need for a basic primer on ICA that can be used by readers of varying levels of mathematical sophistication, including engineers, cognitive scientists, and neuroscientists who need to know the essentials of this evolving method.An overview establishes the strategy implicit in ICA in terms of its essentially physical underpinnings and describes how ICA is based on the key observations that different physical processes generate outputs that are statistically independent of each other. The book then describes what Stone calls "the mathematical nuts and bolts" of how ICA works. Presenting only essential mathematical proofs, Stone guides the reader through an exploration of the fundamental characteristics of ICA.Topics covered include the geometry of mixing and unmixing; methods for blind source separation; and applications of ICA, including voice mixtures, EEG, fMRI, and fetal heart monitoring. The appendixes provide a vector matrix tutorial, plus basic demonstration computer code that allows the reader to see how each mathematical method described in the text translates into working Matlab computer code.

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    I Independent Component Analysis and Blind Source Separation 1 
    1 Overview of Independent Component Analysis
    2 Strategies for Blind Source Separation 13 


    II The Geometry of Mixtures 19 
    3 Mixing and Unmixing 21 
    4 Unmixing Using the Inner Product 31 
    5 Independence and Probability Density Functions 51 


    III Methods for Blind Source Separation 69 
    6 Projection Pursuit 71 
    7 Independent Component Analysis 79 
    8 Complexity Pursuit 111 
    9 Gradient Ascent 119 
    10 Principal Component Analysis and Factor Analysis 129 


    IV Applications 137 
    11 Applications of ICA 139 


    V Appendices 149

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