本文提出一种用于独立成份分析(ICA)的特征选择滤波方案用于改善ICA算法对关键独立成份(SOI)的分离和提取,关键独立成份在其信号样本数据的空间分布上具有一定...
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本文提出一种用于独立成份分析(ICA)的特征选择滤波方案用于改善ICA算法对关键独立成份(SOI)的分离和提取,关键独立成份在其信号样本数据的空间分布上具有一定特征. 本文以平滑滤波为例,表明加入此类特征滤波的ICA算法可以改善对于视觉功能区等平滑图象信号的提取. 因此, 这种特征滤波技术在估计具有平滑特性的脑功能成像方面具有潜在的应用价值.-for Independent component analysis (ICA) feature selection filtering program for the improvement of ICA algorithm independent of the key ingredients (SOI), the separation and extraction, the key element in its independent signal sample data on the spatial distribution of certain characteristics. This paper smoothing filter as an example, table membership in such features filtering ICA algorithm can improve visual function areas smoothing image signal extraction. Therefore, this feature filtering technology is estimated at smoothing properties of brain imaging have potential value.
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