| 1. | A fast kernel - based nonlinear discriminant analysis method
一种基于核的快速非线性鉴别分析方法 |
| 2. | Theory of fisher linear discriminant analysis and its application
线性鉴别分析的理论研究及其应用 |
| 3. | Subspaces discriminant analysis based kernel trick forhuman face recognition
人脸识别中基于核的子空间鉴别分析 |
| 4. | 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 |
| 5. | A new kernel discriminant analysis algorithm and its application to face recognition
一种新的核线性鉴别分析算法及其在人脸识别上的应用 |
| 6. | The technology of nir was applicated in the quality detection for foodstuff such as honey , buckwheat and cooking oil
摘要应用近红外光谱技术对蜂蜜、荞麦、食用油等样品进行品质鉴别分析。 |
| 7. | 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。 |
| 8. | ( 4 ) algebraic feature extraction on the spectro - space has been proposed , which combines the wavelet analysis , wavelet packet analysis and the generalized optimal discriminant analysis
( 4 )提出了一种在频域上的代数特征抽取方法，该方法将小波分析、小波包分析和最优鉴别分析、广义最优鉴别分析相结合。 |
| 9. | How to get the optimal fisher discriminant vectors efficiently in singular case is a very difficult and critical problem . in this paper , we try to solve this problem in theory
该文从理论上解决了奇异情况下基于fisher准则的最优鉴别矢量集的求解问题，为高维、小样本情况下线性鉴别分析方法建立了一个统一的理论框架。 |
| 10. | Based on this idea , a general framework for linear analysis in singular case is developed , i . e . , pca is first used to reduce the dimension of image space to m ( the rank of the total scatter matrix )
更为重要的是，我们进一步揭示了高维、小样本情况下线性鉴别分析的本质，即先作k - l变换，再用fisher鉴别变换作二次特征抽取。 |
