CAJ | 학술논문

提出一种二维线性大间距判别分析（Two dimensional linear maximum margin discriminant analysis,2DLMMDA）的投影算法。该算法一方面采用了有效且稳定的大间距优化准则，引入了Laplacian矩阵，保持了特征矩阵的流形结构，且优化域为Laplacian类间散度与Laplacian类内散度之差，能克服Fisher准则带来的小样本问题；另一方面，采用了具有监督信息的判别分析，大大地提高了识别率。为了验证所提出的算法对特征提取的有效性，选择最近邻分类器进行特征分类，最后通过在CASIA(B)步态库上实验。实验结果表明，文中提出的算法具有更高的识别率和识别速度。
제출일충이유선성대간거판별분석（Two dimensional linear maximum margin discriminant analysis,2DLMMDA）적투영산법。해산법일방면채용료유효차은정적대간거우화준칙，인입료Laplacian구진，보지료특정구진적류형결구，차우화역위Laplacian류간산도여Laplacian류내산도지차，능극복Fisher준칙대래적소양본문제；령일방면，채용료구유감독신식적판별분석，대대지제고료식별솔。위료험증소제출적산법대특정제취적유효성，선택최근린분류기진행특정분류，최후통과재CASIA(B)보태고상실험。실험결과표명，문중제출적산법구유경고적식별솔화식별속도。
In this paper, a novel projection algorithm named 2D linear maximum margin discriminant analysis is proposed. The efficient and stationary maximum margin optimization criterion was used in this algorithm, which introduces Laplacian matrix in order to maintain the manifold structure of the feature matrix, and the optimization criterion is the difference of the Laplacian inter-class scatter and Laplacian intra-class scatter. This algorithm can avoid the small sample size (SSS) problem brought by the Fisher criterion. The discriminant analysis is adopted, which has supervision information and greatly improves the recognition accuracy. In order to verify the efficiency of the proposed method for feature extraction, experiment with the nearest neighborhood (NN) classifier on the CASIA(B) database is conducted. The results show that the proposed method gains a higher recognition rate and faster speed.