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| 核二维子类鉴别分析 | |
| 引用本文： | 王莹,李文辉,傅博,林逸峰,倪洪印.核二维子类鉴别分析[J].电子学报,2013,41(5):992-996. |
| 作者姓名： | 王莹 李文辉 傅博 林逸峰 倪洪印 |
| 作者单位： | 1. 吉林大学汽车仿真与控制国家重点实验室,吉林长春130012;吉林大学计算机科学与技术学院,吉林长春130012 2. 吉林大学计算机科学与技术学院,吉林长春,130012 3. 常州大学信息科学与工程学院,江苏常州,213000 |
| 摘 要： | 针对图像数据的多子类线性不可分问题,提出一种快速核二维子类鉴别分析算法(K2DSDA).算法从理论证明K2DSDA等价于基于核样本列(行)向量的2DSDA,并结合快速核样本图像近似定义实现最优判别向量计算,降低了算法复杂度.实验显示,本文算法在多个标准人脸库上的分类准确率优于其他鉴别分析算法,这证实了K2DSDA的有效性. |
| 关 键 词： | 鉴别分析 核方法 二维子类鉴别分析 多子类 线性不可分 |
| 收稿时间： | 2011-12-06 |
Kemel Two-Dimensional Subclass Discriminant Analysis |
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| WANG Ying , LI Wen-hui , FU Bo , LIN Yi-feng , NI Hong-yin.Kemel Two-Dimensional Subclass Discriminant Analysis[J].Acta Electronica Sinica,2013,41(5):992-996. | |
| Authors: | WANG Ying LI Wen-hui FU Bo LIN Yi-feng NI Hong-yin |
| Affiliation: | 1. State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun,Jilin 130012,China; 2. College of Computer Science and Technology,Jilin University,Changchun,Jilin 130012,China; 3. College of Information Science and Engineering,Changzhou University,Changzhou,Jiangsu 213000,China |
| Abstract: | Aiming to resolve the classification problem that image samples are multi-subclass distributed and non-linearly separable,a kernel two-dimensional subclass discriminant analysis algorithm (K2DSDA) is proposed.In this paper,it has shown that K2DSDA algorithm is theoretically equivalent to column/row-2DSDA based algorithm on kernel samples.Meanwhile,the optimal discriminant vectors are computed via the approximate kernel samples,so that the computational complexity is greatly reduced.The experimental results which tested on benchmark face database show that the proposed algorithm is superior to other state-of-the-art discriminant analysis algorithms,which confirms the effectiveness of K2DSDA. |
| Keywords: | discriminant analysis kernel method two-dimensional subclass discriminant analysis(2DSDA) multi-subclass non-linearly separable |
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