| 改进的线性判别分析算法 |
| |
| 引用本文： | 刘忠宝,王士同.改进的线性判别分析算法[J].计算机应用,2011,31(1):250-253. |
| |
| 作者姓名： | 刘忠宝 王士同 |
| |
| 作者单位： | 1. 江南大学信息学院;山西大学商务学院信息工程系2. 江南大学 信息工程学院 |
| |
| 基金项目： | 国家863计划项目(2007AA1Z158;2006AA10Z313); 国家自然科学基金资助项目(60773206;60704047) |
| |
| 摘 要： | 线性判别分析是一种有效的特征提取方法，但其存在两个缺陷：小样本问题和秩限制问题。为了解决上述问题，提出一种改进的线性判别分析算法ILDA。该方法引进类间离散度标量和类内离散度标量，通过求解样本各维的权值达到特征提取的目的。若干标准人脸数据集和人工数据集上的实验表明ILDA在特征提取方面的有效性。
|
| 关 键 词： | 特征提取 线性判别分析 类间离散度标量 |
| 收稿时间： | 2010-07-20 |
| 修稿时间： | 2010-09-27 |
Improved linear discriminant analysis method |
| |
| LIU Zhong-bao,WANG Shi-tong.Improved linear discriminant analysis method[J].journal of Computer Applications,2011,31(1):250-253. |
| |
| Authors: | LIU Zhong-bao WANG Shi-tong |
| |
| Affiliation: | LIU Zhong-bao1,2,WANG Shi-tong1(1.School of Internet of Things Engineering,Jiangnan University,Wuxi Jiangsu 214122,China,2.School of Information,Business College of Shanxi University,Taiyuan Shanxi 030031,China) |
| |
| Abstract: | Linear Discriminant Analysis(LDA) is an effective feature extraction method,but it has at least two critical drawbacks:small sample size and rank limitation.In order to solve the above problems,this paper presented an improved LDA algorithm(ILDA),which introduced between-class scatter scalar and within-class scatter scalar and extracted features through computing the weight of each dimension in sample space.The experimentl results on ORL facial database and man-made datasets show ILDA achieves good performa... |
| |
| Keywords: | feature extraction Linear Discriminant Analysis(LDA) between-class scatter scalar within-class scatter scalar |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《计算机应用》浏览原始摘要信息 |
|
点击此处可从《计算机应用》下载全文 |
