基于greedy GDA的训练数据减少和非线性特征提取方法

针对大数据集情况下标准广义判别分析（GDA）方法进行非线性特征提取时计算复杂度较高的问题,提出了基于GGDA（greedy GDA）的训练数据减少和非线性特征提取方法.该方法用greedy核主成分分析方法的greedy技术对训练数据选取子集;然后用GDA方法对子集而不是全部训练数据训练特征提取模型;并用几种特征提取的数据进行分类对比实验.实验结果表明,GGDA和GDA方法的特征提取性能优于其他对比方法,GGDA方法不仅较好地保持了GDA方法的特征提取性能,而且减少了大数据集进行非线性特征提取的计算复杂度.

Greedy GDA method for training data reduction and nonlinear featureextraction

Nonlinear feature extraction using standard generalized discriminant analysis(GDA) has high computational complexity in large datasets.Therefore,a greedy GDA(GGDA) is proposed to reduce training data and deal with the nonlinear feature extraction problem.Firstly,a subset is selected from the full training data by using the greedy technique of the greedy KPCA(GKPCA) method.Then,the feature extraction model is trained by using the GDA method with the subset instead of the full training data.Finally,classifica...