将独立分量分析(Independen t Com ponen t A na lys is,ICA)作为人脸特征提取方法。ICA所提取的特征分类能力强、相互独立,对像素间高阶统计特性敏感,并且不易受光照变化的影响。实验结果表明,基于ICA的人脸特征提取方法的识别性能优...将独立分量分析(Independen t Com ponen t A na lys is,ICA)作为人脸特征提取方法。ICA所提取的特征分类能力强、相互独立,对像素间高阶统计特性敏感,并且不易受光照变化的影响。实验结果表明,基于ICA的人脸特征提取方法的识别性能优于特征脸法。针对传统的ICA算法(In form ax算法)存在迭代次数多,难收敛,并且需要人工设定步长来调整学习速度的不足,本文采用F astICA作为ICA的快速算法,并将其关键迭代步骤加以改进,减少了耗时的雅可比矩阵求逆的运算次数。所提出的改进的F astICA具有无需人工参与,收敛速度快,迭代次数少的优点。在特征选择方面,本文将遗传算法(G enetic A lgorithm,GA)应用到独立分量的选择与优化中,从而在保证较高识别性能的前提下,获得最优的人脸特征子集。展开更多
A method was demonstrated based on Infomax independent component analysis(Infomax ICA) for automatically extracting auditory P300 signals within several trials. A signaling equilibrium algorithm was proposed to enhanc...A method was demonstrated based on Infomax independent component analysis(Infomax ICA) for automatically extracting auditory P300 signals within several trials. A signaling equilibrium algorithm was proposed to enhance the effectiveness of the Infomax ICA decomposition. After the mixed signal was decomposed by Infomax ICA, the independent component(IC) used in auditory P300 reconstruction was automatically chosen by using the standard deviation of the fixed temporal pattern. And the result of auditory P300 was reconstructed using the selected ICs. The experimental results show that the auditory P300 can be detected automatically within five trials. The Pearson correlation coefficient between the standard signal and the signal detected using the proposed method is significantly greater than that between the standard signal and the signal detected using the average method within five trials. The wave pattern result obtained using the proposed algorithm is better and more similar to the standard signal than that obtained by the average method for the same number of trials. Therefore, the proposed method can automatically detect the effective auditory P300 within several trials.展开更多
文摘将独立分量分析(Independen t Com ponen t A na lys is,ICA)作为人脸特征提取方法。ICA所提取的特征分类能力强、相互独立,对像素间高阶统计特性敏感,并且不易受光照变化的影响。实验结果表明,基于ICA的人脸特征提取方法的识别性能优于特征脸法。针对传统的ICA算法(In form ax算法)存在迭代次数多,难收敛,并且需要人工设定步长来调整学习速度的不足,本文采用F astICA作为ICA的快速算法,并将其关键迭代步骤加以改进,减少了耗时的雅可比矩阵求逆的运算次数。所提出的改进的F astICA具有无需人工参与,收敛速度快,迭代次数少的优点。在特征选择方面,本文将遗传算法(G enetic A lgorithm,GA)应用到独立分量的选择与优化中,从而在保证较高识别性能的前提下,获得最优的人脸特征子集。
基金Projects(81460273,61265006)supported by the National Natural Science Foundation of ChinaProject(2013GXNSFAA019325)supported by Guangxi Natural Science Foundation,ChinaProject(1348020-10)supported by Guangxi Science and Technology Program,China
文摘A method was demonstrated based on Infomax independent component analysis(Infomax ICA) for automatically extracting auditory P300 signals within several trials. A signaling equilibrium algorithm was proposed to enhance the effectiveness of the Infomax ICA decomposition. After the mixed signal was decomposed by Infomax ICA, the independent component(IC) used in auditory P300 reconstruction was automatically chosen by using the standard deviation of the fixed temporal pattern. And the result of auditory P300 was reconstructed using the selected ICs. The experimental results show that the auditory P300 can be detected automatically within five trials. The Pearson correlation coefficient between the standard signal and the signal detected using the proposed method is significantly greater than that between the standard signal and the signal detected using the average method within five trials. The wave pattern result obtained using the proposed algorithm is better and more similar to the standard signal than that obtained by the average method for the same number of trials. Therefore, the proposed method can automatically detect the effective auditory P300 within several trials.
