基于最近特征线的二维非参数化判别分析算法  

Two-dimensional Nonparametric Discriminant Analysis Algorithm Based on Nearest Feature Line

作　　者：张旭[1] 张向群[2] 赵伟[1] 何岩峰[1] 

机构地区：[1]中国人民解放军71426部队 [2]许昌学院计算机科学与技术学院

出　　处：《计算机工程》2012年第14期171-172,176,共3页Computer Engineering

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。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.

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 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]