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pca算法的matlab实现 主成分分量分析可用于数据的降维和模式识别问题 -pca algorithm matlab component analysis to achieve the principal component can be used for data dimensionality reduction and pattern recognition problem
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lda 源码。用来为数据降维，使得识别率有所提高。-lda source code. Is used for data dimensionality reduction, making the recognition rate increased.
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用于特征降维，特征融合，相关分析等多元数据分析的典型相关分析Matlab代码实现。-For feature reduction, feature fusion, correlation analysis, multivariate data analysis, canonical correlation analysis of Matlab code implementation.
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用于特征降维，特征融合，相关分析等多元数据分析的鉴别型典型相关分析（DCCA）Matlab代码实现。-For feature reduction, feature fusion, multivariate data analysis and correlation analysis based identification of canonical correlation analysis (DCCA) Matlab code implementation.
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用于特征降维，特征融合，相关分析等多元数据分析的广义典型相关分析（GCCA）Matlab代码实现。-For feature reduction, feature fusion, correlation analysis, multivariate data analysis using generalized canonical correlation analysis (GCCA) Matlab code implementation.
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用于特征降维，特征融合，相关分析等多元数据分析的fisher鉴别分析（FLDA）Matlab代码实现。-For feature reduction, feature fusion, correlation analysis, multivariate data analysis of the fisher discriminant analysis (FLDA) Matlab code implementation.
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用于特征降维人脸识别等多元数据分析的主分量分析投影的Matlab代码实现。-For feature reduction and other multivariate data analysis, face recognition principal component analysis projection of the Matlab code implementation.
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