欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
线性鉴别分析中处理小样本问题的方法有两类：①在模式识别之前，通过降低模式样本特征向量的维数达到消除奇异性的目的；②发展算法获得低维鉴别特征。将这两种方法结合起来，解决了高维小样本情况下基于广义Fisher线性鉴别准则的不相关最优鉴别矢量集的求解问题，给出了抽取最优鉴别矢量的有效算法。
英文摘要：
Nowadays there are two kinds of methods for dealing with the problems of small sample size in linear discriminant analysis. One is that the aim of avoiding singularity is arrived by dimension reduction of feature vector of pattern samples before pattern recognition. The other is to develop an algorithm to gain the lower discriminant features. By combining the above two kinds of methods, the problem has been solved that how to gain the optimal set of uncorrelated discriminant vectors for small sample size problem based on the generalized Fisher＇ s linear discriminant criterion. An efficient algorithm has been presented in this paper.
同期刊论文项目
同项目期刊论文
期刊信息
位置：