1) kernel uncorrelated discriminant analysis
核不相关鉴别分析
1.
Based on uncorrelated discriminant analysis, kernel uncorrelated discriminant analysis is developed.
核不相关鉴别分析是在线性不相关鉴别分析的基础上发展起来的·然而,由于核函数的运用,计算核不相关矢量集变得更加复杂·为了解决这个问题,提出一种解决核不相关鉴别分析的有效算法·该算法巧妙地利用了矩阵的分解,然后在一个矩阵对上进行广义奇异值分解·与此同时,提出了几个相关的定理·最重要的是,提出的算法能克服核不相关鉴别分析中矩阵的奇异问题·在某种意义上,提出的算法拓宽了已有的算法,即从线性问题到非线性问题·最后,用手写数字字符识别实验来验证提出的算法是可行和有效的
2) uncorrelated discriminant analysis
不相关鉴别分析
1.
However,the computational cost of uncorrelated discriminant analysis is very high.
不相关鉴别分析是一种非常有效并起着重要作用的线性鉴别分析方法,它能抽取出具有不相关性质的特征分量。
2.
A novel method based on weighted uncorrelated discriminant analysis for face recognition was proposed.
提出了一种基于加权Fisher的不相关鉴别分析的人脸识别方法。
3) kernel discriminant analysis
核鉴别分析
1.
Then,the Gabor features are extracted with an improved kernel discriminant analysis method in order to solve the singularity problem of kernel within-class scatter matrix which is caused by the small sample size problem of expression recognition.
首先对新生儿面部图像进行Gabor变换,然后针对变换后的Gabor特征,用一种改进的核鉴别分析方法对它进行二次特征提取。
4) kernel uncorrelated nonlinear discriminant analysis
核非线性不相关辨别分析
5) Uncorrelated Discriminates Vectors
不相关鉴别矢量
6) kernel Fisher discriminant analysis
核Fisher鉴别分析
1.
Feature extraction based on rough kernel Fisher discriminant analysis and its application on aeroengine fault diagnosis;
基于粗糙核Fisher鉴别分析的特征提取及其在发动机故障诊断中的应用
2.
Based on the idea of isomorphic mapping, We proposed an optimal kernel Fisher discriminant analysis (OKFDA), from which we acquire a general algorithm for the computation of the .
基于同构映射的思想,我们提出了一种最优的核Fisher鉴别分析(OKFDA)方法,从理论上巧妙的解决了奇异情况下最优鉴别矢量集的求解问题。
补充资料：相关分析(见统计分析)
相关分析(见统计分析)
x Iangguan fenxj相关分析见统计分析。
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参考词条