半监督超图正则拉普拉斯降维条件下分类算法广义界分析

摘要

In this paper, the statistical characteristics of hypergraph classification algorithm under semi-supervised nor-malization laplacian dimension reduction were studied. By defining projection operator on the feature space, it is veri-fied that vector after dimension reduction well approximate to the original target vector. The generalization bound for classification algorithm under dimension reduction is estimated by using the trick of matrix trace.%　　研究超图上半监督正则拉普拉斯降维框架下分类算法的统计特征，通过特征空间上的映射算子说明降维后得到的向量较好地逼近原目标向量。利用矩阵迹的特性给出降维条件下分类算法广义界估计值。