| 1. | An improved optimal set of statistical uncorrelated discriminant vectors
改进的统计不相关最优鉴别矢量集 |
| 2. | Kernel mapping based algorithm for uncorrelated discriminant vectors set
基于核映射的无相关鉴别矢量集算法 |
| 3. | A study on the essence of optimal statistical uncorrelated discriminant vectors
统计不相关最佳鉴别矢量集的本质研究 |
| 4. | A unified algorithm for the computation of statistically uncorrelated optimal discriminant vectors
求解统计不相关的最佳鉴别矢量的统一算法 |
| 5. | A theoretical result on the generalized optimal setof statistically uncorrelated discriminant vectors
广义统计不相关最优鉴别矢量集的一个理论结果 |
| 6. | An iterative algorithm of solving generalized optimal set of discriminant vectors based on perturbation
基于扰动方法的广义最佳鉴别矢量集求解的一种迭代算法 |
| 7. | An analytical algorithm of solving generalized optimal set of discriminant vectors based on perturbation method
基于扰动方法的广义最佳鉴别矢量集求解的一种解析算法 |
| 8. | An iterative algorithm for the generalized optimal set of discriminant vectors and its application to face recognition
求解广义最佳鉴别矢量集的一种迭代算法及人脸识别 |
| 9. | A novel algorithm calculation the generalezed optimal set of discriminant vectors in the cace of small number of samples and its application t to face recognition
一种在小样本情形下求解广义最佳鉴别矢量集的新算法及其在人脸识别中的应用 |
| 10. | 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。 |
