支持向量数据描述鉴别分析

Support vector data description discriminant analysis

结合支持向量机(Support vector machine,SVM)最大类间间隔和支持向量数据描述(Support vector data description,SVDD)最小类内体积,提出支持向量数据描述鉴别分析(Support vector data description discriminant analysis,SVDDDA)。SVDDDA构造两大小同心超球,小超球包含正类样本,大超球排除负类样本,最大化两超球间隔,同时压缩正负类所处特征空间体积,利用样本距超球心距离定义了投影坐标。SVDDDA不仅能够获取类间鉴别信息,还能够获取类内散布信息。最后,通过人脸表情识别试验验证了该算法的有效性。

Based on the maximum inter-class margin of Support Vector Machine（SVM） and the minimum intra-class volume of Support Vector Data Description（SVDD）,a discriminant algorithm is proposed,named Support Vector Data Description Discriminant Analysis（SVDDDA）.This algorithm establishes two different concentric hyperspheres.The positive class samples are packed in the small hypersphere and the negative class samples are excluded from the large hepersphere.The objective function of the model maximizes the inter-class margin and minimizes the volume of the small hypersphere simultaneously.The projection coordinates are defined by the distance between the sample and the center of the hyperspheres.SVDDDA can preserve the inter-class discriminant information and intra-class scatter distribution.Results of experiment on public facial expression database demonstrate the efficiency of the proposed method.