基于支持向量信息测度排序的快速分类算法

如何降低支持向量机海量训练样本的数目,是提高算法速度的关键。提出利用支持向量分布的几何特征建立基于特征空间中支持向量信息测度的快速算法,对于训练样本首先进行基于支持向量信息测度升序排序处理,然后根据训练样本提供的信息测度选择合适的训练样本子空间,在该样本子空间内采用乘性规则直接求取Lagrange因子,而不是传统的二次优化方法;最后针对附加残余样本进行交叉验证处理,直到算法满足收敛性准则。各种分类实验表明,提出的算法具有较好的性能,特别是在训练样本庞大、支持向量数量较多的情况下,能够较大幅度地减少计算复杂度,提高分类速度。

Fast classification algorithm based on the sort of support vector information measure

To improve the training speed performance of large-scale support vector machine(SVM), a fast algorithm is proposed by exploiting the geometric distribution of support vector in feature space. A support vector information measure definition is set up and a sort process is presented. Then a reduced number of sample subspace is extracted for support vector training. In addition, instead of the traditional quadratic programming, multiplicative update is used to solve Lagrange multiplier in optimization the solution of support vector. The samples of rest are used for cross validating till the algorithm is convergence. Experimental results demonstrate that this method has better performance and overcome the flaw of standard SVM. This algorithm could greatly reduce the computational load and increase the speed of training, especially in the case of large number of training sample.