基于Parzen窗的高阶统计量特征降维方法

高阶统计量通常能比低阶统计量提取更多原数据的信息，但是较高的阶数带来了较高的时间复杂度.基于Parzen窗估计构造了高阶统计量，通过论证得出:对于所提出的核协方差成分分析（KCCA）方法，通过调节二阶统计量广义D vs E的参数就能够达到整合高阶统计量的目的，而无需计算更高阶统计量.即核协方差成分分析方法能够对高阶统计量的特征降维的同时，又不增加计算复杂性.

Feature reduction of high-order statistics based on Parzen window

The high order statistics method can often extract more information regarding original data than a low order statistics; yet in the meantime create higher time complexity. The high order statistics methods were constructed by utilizing estimation based on Parzen window. It was revealed that the kernel covariance component analysis (KCCA) method proposed earlier by the researchers, contained useful information on the high order statistics and could be obtained by only adjusting the parameters of the proposed generalized D vs E. Also based on the second order statistics, the heavy computational burden about the high order statistics can be avoided. That is to say, the KCCA method can accomplish the feature reduction of high order statistics without increasing its computational complexity.