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提出了将信号进行相空间重构后再采用奇异值分解, 对分解后的主成分进行包络分析, 从而提取信号的隐含特
征的方法, 并将该方法应用于齿轮的局部故障振动特征信号的提取中。数值仿真实验结果表明, 该方法能有效提取强背景
信号及噪声中的弱冲击特征信号, 是一种有效的弱信号特征提取方法。采用该方法对齿轮振动信号进行故障特征提取与识
别, 结果与实际情况相符。-Signal implicit characteristic of phase space reconstruction, and th
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为有效解决局部二元模式(LBP)在人脸识别特征提取时维数过高的问题，提出了一种结合LBP特征和主成分分析(PCA)的人脸识别方法．-To effectively solve the local binary pattern (LBP) feature extraction in face recognition problem of high dimensionality, we propose a combination of LBP features and principal compon
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基于主成分分析的人脸特征提取的MATLAB源代码，通过测试的-Principal component analysis based on human face feature extraction MATLAB source code, pass the test
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this principle component analysis(PCA) that is used for linear dimensionality reduction and feature extraction as well as the nonlinear form of PCA which is KPCA and how PCA is used for Active shape models -this is principle component analysis(PCA) t
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是学习PCA特征提取的很好的学习资料，主同步信号PSS在时域上的相关仿真，多元数据分析的主分量分析投影。- Is a good learning materials to learn PCA feature extraction, PSS primary synchronization signal in the time domain simulation related, Principal component analysis of multivariate data analysis p
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