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Titlebook: Independent Component Analysis and Blind Signal Separation; Fifth International Carlos G. Puntonet,Alberto Prieto Conference proceedings 2

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发表于 2025-3-21 16:09:47 | 显示全部楼层 |阅读模式
书目名称Independent Component Analysis and Blind Signal Separation
副标题Fifth International
编辑Carlos G. Puntonet,Alberto Prieto
视频videohttp://file.papertrans.cn/464/463378/463378.mp4
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Independent Component Analysis and Blind Signal Separation; Fifth International  Carlos G. Puntonet,Alberto Prieto Conference proceedings 2
出版日期Conference proceedings 2004
关键词Bayesian learning; Derivative; ICA algorithm; Maximum; Minimum; blind source separation; calculus; differen
版次1
doihttps://doi.org/10.1007/b100528
isbn_softcover978-3-540-23056-4
isbn_ebook978-3-540-30110-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2004
The information of publication is updating

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书目名称Independent Component Analysis and Blind Signal Separation网络公开度学科排名




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书目名称Independent Component Analysis and Blind Signal Separation年度引用学科排名




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发表于 2025-3-21 20:59:33 | 显示全部楼层
The Minimum Support Criterion for Blind Signal Extraction: A Limiting Case of the Strengthened Youngs the extraction even when the sources are non identically distributed. Another interesting feature is that it is robust to the presence of certain kinds of additive noise and outliers in the observations.
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Gaussianizing Transformations for ICAnt sources from a linear mixture by specifically utilizing a Gaussianizing nonlinearity is demonstrated. The link between the proposed topology and nonlinear principal components is established. Possible extensions to nonlinear mixtures and several implementation issues are also discussed.
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Accurate, Fast and Stable Denoising Source Separation Algorithmsariance based denoising function is proposed. Estimates of variances of different source are whitened which actively promotes separation of sources. Experiments with artificial data and real magnetoencephalograms demonstrate that the developed algorithms are accurate, fast and stable.
发表于 2025-3-22 17:24:36 | 显示全部楼层
Analytical Solution of the Blind Source Separation Problem Using Derivativestion to its simplicity, the method is able to separate Gaussian sources, since it only requires second order statistics. For two mixtures of two sources, the analytical solution is given, and a few experiments show the efficiency of the method for the blind separation of two Gaussian sources.
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Approximate Joint Diagonalization Using a Natural Gradient Approachithms are in the ability to accommodate non-positive-definite matrices (compared to Pham’s algorithm), in the low computational load per iteration (compared to Yeredor’s AC-DC algorithm), and in the theoretically guaranteed convergence to a true (possibly local) minimum (compared to Ziehe .’s FFDiag algorithm).
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