基于支持向量机和线性判别分析的维数约减方法及其应用

目前已提出的一些基于支持向量机的维数约减方法，但其投影矩阵的获得仅考虑支持向量机的类间间隔，而忽略了数据中的类内信息。本文首次提出了一种基于支持向量机和线性判别分析的维数约减方法，称之为DRSL。DRSL实现了类问和类内信息的有效组合，能有效拟合数据中类间和类内结构，使得所获投影矩阵能够提高后续分类器的推广能力。实验验证了该方法的有效性。

DIMENSIONALITY REDUCTION BASED ON SVM AND LDA, AND ITS APPLICATION TO CLASSIFICATION TECHNIQUE

Some dimensionality reduction (DR) approaches based on support vector machine (SVM) are propo-sed.But the acquirement of the projection matrix in these approaches only considers the between-class margin based on SVM while ignoring the within-class information in data.This paper presents a new DR approach, call-ed the dimensionality reduction based on SVM and LDA (DRSL).DRSL considers the between-class margins from SVM and LDA, and the within-class compactness from LDA to obtain the projection matrix.As a result, DRSL can realize the combination of the between-class and within-class information and fit the between-class and within-class structures in data.Hence, the obtained projection matrix increases the generalization ability of sub-sequent classification techniques.Experiments applied to classification techniques show the effectiveness of the proposed method.