一种基于马氏距离的支持向量快速提取算法

A fast algorithm for extracting the support vector on the Mahalanobis distance

针对用支持向量机解决分类问题,提出了一种采用样本到某一类的马氏距离来提取可能为支持向量的数据的方法,同时阐明了如何解决在输入空间和特征空间中求马氏距离所遇到的问题.利用特征值、特征矢量及伪逆运算的并行计算方法,建立了一种提取支持向量的快速算法.用该方法对训练数据进行预处理后,可以加快支持向量机的训练速度.实验结果也表明了该方法的有效性.

A method for extracting training data which most probably are support vectors for SVM by the Mahalanobis distance from a vector to a class is presented. How to compute Mahalanobis distance in the input and feature space is described in detail. The algorithm is fast since there are efficient methos for finding eigenvalues and eigenvectors of a symmetric matrix or computing pseudoinversion involved in finding the Mahalanobis distance. The training time for SVM can be reduced when the training set is preprocessed in this way. Experimental results illustrate its effectiveness.