基于最大间距准则(MMC)新的有效特征提取方法

New and Efficient Feature Extraction Methods Based on Maximum Margin Criterion

基于最大间距准则(Maximum Margin Criterion,MMC)下,提出一组具有标准正交性的最佳鉴别矢量的计算方法和一组具有统计不相关性的最佳鉴别矢量的计算方法。这种方法的目的是寻求一组最佳鉴别矢量既要使投影变换后的特征空间的类间散度最大,而类内散度最小;又要减小最佳鉴别矢量间的统计相关性。与原MMC特征提取方法相比,新的特征提取方法降低了甚至消除了最佳鉴别矢量间的统计相关性,提高了识别率。通过分别在ORL人脸库和NUST603人脸库上实验结果表明提出的具有统计不相关性的MMC特征提取方法在识别率方面整体上好于原MMC特征提取方法和常用的主成分分析(PCA)法。另外,揭示了MMC准则特征提取与Fisher准则特征提取的内在关系。

Based on the maximum margin criterion （MMC）, a new algorithm of orthogonal optimal discriminant vectors and a new algorithm of statistically uncorrelated optimal discriminant vectors for feature extraction were proposed. The purpose of the maximum margin criterion is to maximize the inter-class scatter while simultaneously minimizing the intra-class scatter after the projection. Compared with original MMC method and principal component analysis （PCA） method, the proposed methods are better in terms of reducing or eliminating the statistically correlation between features and improving recognition rate. The experimental results on Olivetti Research Laboratory （ORL） face database and NUST603 face database show that the new feature extraction method of statistically uncorrelated maximum margin criterion （SUMMC） are better in terms of recognition rate and stability. Besides, the relations between maximum margin criterion and Fisher criterion for feature extraction were revealed.