| 1. | Theory of fisher linear discriminant analysis and its application
线性鉴别分析的理论研究及其应用 |
| 2. | A study on personal credit scoring using linear discriminant analysis
线性判别式分析在个人信用评估中的应用 |
| 3. | A new two - dimensional linear discriminant analysis algorithm based on fuzzy set theory
基于模糊集理论的二维线性鉴别分析新方法 |
| 4. | In this paper , we focus on two - class discriminating problem and chiefly study two types of linear discriminant analysis : principal component classifier ( pcc ) and fisher linear discriminant analysis ( flda )
本文就两分类问题，研究了两种线性判别：主分量分类器和fisher判别分析。 |
| 5. | Linear projection analysis , including principal component analysis ( or k - l transform ) and fisher linear discriminant analysis , is the classical and popular technique for feature extraction
线性投影分析，包括主分量分析(或称k - l变换)和fisher线性鉴别分析，是特征抽取中最为经典和广泛使用的办法。 |
| 6. | A face - recognition algorithm based on fisher linear discriminant analysis is studied in detail which combines principal component analysis ( pca ) based eigenface method and linear discriminant analysis ( lda ) method
该方法将基于主成分分析( pca )的特征脸方法和基于线性判别分析( lda )的分类方法有机的结合起来。 |
| 7. | The inherent relationship between fisher linear discriminant analysis and karhunen - loeve expansion is revealed , i . e . , ulda is essentially equivalent to one classical k - l expansion method . moreover , we enhance ulda using the idea of another k - l expansion method , and finally an optimal k - l expansion method is developed
揭示了具有统计不相关性的线性鉴别分析与经典的k - l展开方法的内在关系，即不相关的线性鉴别分析方法与包含在类均值向量中判别信息的最优压缩方法是等价的，并在此基础上导出了一种最优k - l展开方法。 |
| 8. | Feature extraction through 2 - order polynomial fit of the descending part of the response curve made possible a timesaving measurement process . the performances of two pattern recognition algorithms , namely principal component analysis ( pca ) and linear discriminant analysis ( lda ) in practical problems were discussed . artificial neural network ( ann ) was utilized with back - propagation algorithm ( bpa ) , and the combination of pca / lda with ann improved the identification performance of the system
基于对模式识别系统的深入研究，提出了从响应阶段数据提取特征的方法，节省了测试所需时间；比较了主成分分析法( principalcomponentanalysis , pca )与线性判别式法( lineardiscriminantanalysis , lda )两种模式识别方法在实际应用中的不同结果，分析了原因；设计了采用误差反传算法back - propagationalgorithm , bpa )的前向人工神经网络( artificialneuralnetwork , ann ) ，并指出其应用中存在的问题，提出了改进建议；利用pca lda与ann相结合的方法改善了系统的识别性能。 |
| 9. | The conventional principal component analysis ( pca ) and fisher linear discriminant analysis ( lda ) are based on vectors . that is to say , if we use them to deal with the image recognition problem , the first step is to transform original image matrices into same dimensional vectors , and then rely on these vectors to evaluate the covariance matrix and to determine the projector
所提出的这两种方法的共同特点是，在进行图像特征抽取时，不需要事先将图像矩阵转化为高维的图像向量，而是直接利用图像矩阵本身构造图像散布矩阵，然后基于这些散布矩阵进行主分量分析与线性鉴别分析。 |
