核主成分分析中核参数选择的遗传算法

摘要

Kernel principal component analysis can extract the nonlinear features of the data, but the performance is great impacted by the parameter of kernel function. This paper presents a kernel parameters optimization method which based on a piecewise binary encoding. The experimental results are very good by using the approach to optimize the kernel parameters in the case of unknown the distribution of the data, which indicating the effectiveness of the method.%基于核方法的主成分分析虽然能够提取数据的非线性特征,但其性能受核参数的影响比较大.本文提出一种基于遗传算法的核参数优化算法,在未知数据分布特征的情况下,采用该方法对核参数进行优化选取,取得较好的实验效果,表明该方法的有效性.