摘要： 提出一种基于最近特征线(NFL)的二维非参数化判别分析算法，用于人脸识别等模式分类问题。该算法在子空间学习阶段运用NFL思想计算训练集中各样例的最近特征距离，计算得到低维投影空间，在低维投影空间中进行分类。通过ORL标准人脸数据库进行实验，结果表明该算法的鲁棒性优于传统算法。
关键词:
最近特征线,
二维非参数化判别分析,
子空间学习,
ORL数据库
Abstract: A new subspace learning algorithm called Two-dimensional Nonparametric Discriminant Analysis Algorithm Based on Nearest Feature Line (TDNDA-NFL) is proposed for pattern classification, such as face recognition. The proposed algorithm integrates the idea of NFL and two-dimensional nonparametric discriminant algorithm. It computes the nearest feature distance based on the idea of NFL in the subspace learning stage, then it computes the low-dimensional subspace using two-dimensional nonparametric discriminant algorithm. It classifies in the projected space. In experiments the proposed method is evaluated by the ORL databases and computed with several state-of-the-art algorithms. According to the computed results, the proposed method outperformes other algorithms.
Key words:
Nearest Feature Line(NFL),
Two-dimensional Nonparametric Discriminant Analysis(TDNDA), subspace learning,
ORL database
中图分类号:
张旭, 张向群, 赵伟, 何岩峰. 基于最近特征线的二维非参数化判别分析算法[J]. 计算机工程, 2012, 38(14): 171-172.
ZHANG Xu, ZHANG Xiang-Qun, DIAO Wei, HE Yan-Feng. Two-dimensional Nonparametric Discriminant Analysis Algorithm Based on Nearest Feature Line[J]. Computer Engineering, 2012, 38(14): 171-172.