{"product_id":"independent-component-analysis-theory-and-applications-von-te-won-lee","title":"Independent Component Analysis","description":"\n                \u003cem\u003eIndependent Component Analysis\u003c\/em\u003e (ICA) is a  signal-processing method to extract independent sources given only  observed data that are mixtures of the unknown sources. Recently,  blind source separation by ICA has received considerable attention  because of its potential signal-processing applications such as speech  enhancement systems, telecommunications, medical signal-processing and  several data mining issues. \u003cbr\u003e  This book presents theories and applications of ICA and includes  invaluable examples of several real-world applications. Based on  theories in probabilistic models, information theory and artificial  neural networks, several unsupervised learning algorithms are  presented that can perform ICA. The seemingly different theories such  as infomax, maximum likelihood estimation, negentropy maximization,  nonlinear PCA, Bussgang algorithm and cumulant-based methods are  reviewed and put in an information theoretic framework to unify  several lines of ICA research. An algorithm is presented that is able  to blindly separate mixed signals with sub- and super-Gaussian source  distributions. The learning algorithms can be extended to filter  systems, which allows the separation of voices recorded in a real  environment (cocktail party problem). \u003cbr\u003e  The ICA algorithm has been successfully applied to many biomedical  signal-processing problems such as the analysis of  electroencephalographic data and functional magnetic resonance imaging  data. ICA applied to images results in independent image components  that can be used as features in pattern classification problems such  as visual lip-reading and face recognition systems. The ICA algorithm  can furthermore be embedded in an expectation maximization framework  for unsupervised classification. \u003cbr\u003e  \u003cem\u003eIndependent Component Analysis: Theory and Applications\u003c\/em\u003e is the  first book to successfully address this fairly new and generally  applicable method of blind source separation. It is essential reading  for researchers and practitioners with an interest in ICA.\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9781441950567\"\u003e\u003ch3\u003eTheory and Applications\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9780792382614\"\u003e\u003ch3\u003eTheory and Applications\u003c\/h3\u003e\u003c\/div\u003e","brand":"Libri","offers":[{"title":"Softcover - 9781441950567","offer_id":39415612899421,"sku":"9781441950567","price":160.49,"currency_code":"EUR","in_stock":true},{"title":"Hardcover - 9780792382614","offer_id":50828749766,"sku":"9780792382614","price":160.49,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/186efa2a-d87e-43e4-9692-57c1b3266da4.jpg?v=1751603216","url":"https:\/\/shop.autorenwelt.de\/products\/independent-component-analysis-theory-and-applications-von-te-won-lee","provider":"Autorenwelt Shop","version":"1.0","type":"link"}