| 不相关最佳鉴别矢量集的有效算法 |
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| 引用本文： | 陈伏兵,王文胜,谢永华,杨静宇. 不相关最佳鉴别矢量集的有效算法[J]. 计算机应用研究, 2006, 23(6): 31-33 |
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| 作者姓名： | 陈伏兵 王文胜 谢永华 杨静宇 |
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| 作者单位： | 南京理工大学,计算机科学系,江苏,南京,210094;淮阴师范学院,数学系,江苏,淮安,223001;南京理工大学,计算机科学系,江苏,南京,210094 |
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| 基金项目： | 国家自然科学基金资助项目(60472060)；江苏省自然科学基金资助项目（05KJD520036） |
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| 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。
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| 关 键 词： | 特征抽取 小样本问题 广义线性鉴别分析 不相关鉴别矢量 人脸识别 |
| 文章编号： | 1001-3695（2006）06-0031-03 |
| 收稿时间： | 2005-05-16 |
| 修稿时间： | 2006-02-20 |
Efficient Algorithm to Optimal Set of Uncorrelated Discriminates Vectors |
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| CHEN Fu bing,WANG Wen sheng,XIE Yong hu,YANG Jing yu. Efficient Algorithm to Optimal Set of Uncorrelated Discriminates Vectors[J]. Application Research of Computers, 2006, 23(6): 31-33 |
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| Authors: | CHEN Fu bing WANG Wen sheng XIE Yong hu YANG Jing yu |
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| Affiliation: | (1.Dept. of Computer Science, Nanjing University of Science & Technology, Nanjing Jiangsu 210094, China;2.Dept. of Mathematics, Huaiyin Teachers College, Huaian Jiangsu 223001, China) |
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| Abstract: | 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. |
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| Keywords: | Feature Extraction Small Sample Size Problem Generalized Linear Discriminates Analysis Uncorrelated Discriminates Vectors Face Recognition |
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