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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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借鉴了主成分分析算法(PCA),利用matlab GUI实现的串口编程例子,是学习PCA特征提取的很好的学习资料。- It draws on principal component analysis algorithm (PCA), Use serial programming examples matlab GUI implementation, Is a good learning materials to learn PCA feature extraction.
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是学习PCA特征提取的很好的学习资料,包括面积、周长、矩形度、伸长度,多元数据分析的主分量分析投影。- Is a good learning materials to learn PCA feature extraction, Including the area, perimeter, rectangular, elongation, Principal component analysis of multivariate data analysis projection.
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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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基于核的主成分分析是一种非线性特征提取方法,它通过一个非线性映射将数据从输入空间映射到特征空间,然后在特征空间中进行通常的主成分分析,其中的内积运算采用一个核函数来代替-Core-based principal component analysis is a nonlinear feature extraction method, which maps data the input space to the feature space through a nonlinear mapping,
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music高阶谱分析算法,包括主成分分析、因子分析、贝叶斯分析,是学习PCA特征提取的很好的学习资料。- music higher order spectral analysis algorithm, Including principal component analysis, factor analysis, Bayesian analysis, Is a good learning materials to learn PCA feature extraction.
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用于图像识别和特征提取时的主成分分析程序,采用Matlab编写,-When used in image recognition and feature extraction of principal component analysis procedure, using Matlab,
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用于图像识别和特征提取时的主成分分析程序,采用Matlab编写,-When used in image recognition and feature extraction of principal component analysis procedure, using Matlab,
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用于图像识别和特征提取时的主成分分析程序,采用Matlab编写,-When used in image recognition and feature extraction of principal component analysis procedure, using Matlab,
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PCA源程序,主元分析源程序,可以用于变量的特征提取(PCA source code, principal component analysis source, can be used for variable feature extraction)
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PIVlab - 时间分辨粒子图像测速(PIV)工具:
一种基于GUI的工具,用于预处理,分析,验证,后处理,可视化和模拟PIV数据。
使用MATLAB网络研讨会进行人脸识别代码:
使用MATLAB在线讲座的人脸识别中的主要演示文件。
Gabor特征提取:
该程序生成一个自定义Gabor滤波器组; 并使用它们提取图像特征。
主成分分析:
用于特征提取;
链码:
基于MATLAB的freeman的曲面轮廓描述(PIVlab - time resolved particle
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用于图像识别和特征提取时的主成分分析程序,采用Matlab编写,(When used in image recognition and feature extraction of principal component analysis procedure, using Matlab,)
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Principal component analysis of multivariate data analysis projection, PSS primary synchronization signal in the time domain simulation related, Is a good learning materials to learn PCA feature extraction.
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Weighted acceleration, It draws on principal component analysis algorithm (PCA), For feature extraction, signal de-noising.
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Contains the eigenvalue and eigenvector extraction, the training sample, and the final recognition, Principal component analysis of multivariate data analysis projection, For feature extraction, signal de-noising.
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主成分分析的一种改进算法,是一种非线性的特征提取方法。(An improved algorithm of principal component analysis is a nonlinear feature extraction method)
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该方法利用人脸具有镜像对称的自然特性,依据奇偶分解原理,生成成镜像奇、偶对称样本,井利用人脸对称图像作为训练样本,再利用主分量分析(PCA)对训练样本进行二阶相关和降维处理,然后对处理后的样本进行ICA特征提取。理论和分析实验证明,该算法有效减线了人脸受到视角、光照、人脸表情、姿势变化等因素的最响,又增加了训练样本容量,减少了计算复杂度,同时有效解决了小样本问题,提高了识别率.(The method uses the natural characteristics of mirror symme
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一种特征提取方法:结合主元分析(PCA)和核主元分析(KPCA)的前馈神经网络(FNN)(A feature extraction method: the feedforward neural network (FNN) combined with principal component analysis (PCA) and kernel principal component analysis (KPCA))
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NMF、PCA-人脸图像特征抽取与对比,图像识别,主成分分析(Face image feature extraction and comparison, image recognition, principal component analysis)
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