| 書目名稱 | Independent Component Analysis | | 副標(biāo)題 | Theory and Applicati | | 編輯 | Te-Won Lee | | 視頻video | http://file.papertrans.cn/464/463377/463377.mp4 | | 圖書封面 |  | | 描述 | .Independent Component Analysis. (ICA) is asignal-processing method to extract independent sources given onlyobserved data that are mixtures of the unknown sources. Recently,blind source separation by ICA has received considerable attentionbecause of its potential signal-processing applications such as speechenhancement systems, telecommunications, medical signal-processing andseveral data mining issues. .This book presents theories and applications of ICA and includesinvaluable examples of several real-world applications. Based ontheories in probabilistic models, information theory and artificialneural networks, several unsupervised learning algorithms arepresented that can perform ICA. The seemingly different theories suchas infomax, maximum likelihood estimation, negentropy maximization,nonlinear PCA, Bussgang algorithm and cumulant-based methods arereviewed and put in an information theoretic framework to unifyseveral lines of ICA research. An algorithm is presented that is ableto blindly separate mixed signals with sub- and super-Gaussian sourcedistributions. The learning algorithms can be extended to filtersystems, which allows the separation of voices recorded in a realenvir | | 出版日期 | Book 1998 | | 關(guān)鍵詞 | Independent Component Analysis; algorithms; blind source separation; classification; cognition; communica | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4757-2851-4 | | isbn_softcover | 978-1-4419-5056-7 | | isbn_ebook | 978-1-4757-2851-4 | | copyright | Springer-Verlag US 1998 |
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