基于一种自适应核学习的KECA子空间故障特征提取

A Method for Feature Extraction in KECA Feature Subspace Based on Adaptive Kernel Learning

核属性约简方法对于去除冗余信息,调整数据非线性结构具有独特的优势.针对航空电子设备故障诊断中有效特征提取困难,核属性约简方法中核函数与核参数选择繁琐等问题,提出了一种基于自适应核函数优化学习的核熵元分析(kernel entropy component analysis,KECA)特征提取方法.首先针对一种自适应核函数基于改进的Fisher核矩阵测量准则建立了一种面向多分类任务的核函数优化框架,然后将优化结果与KECA相结合,通过在KECA特征子空间中选择对输入数据Renyi熵估计有较大贡献的核矩阵特征向量来实现故障特征提取.实验结果表明,本文方法不仅提升了分类精度,而且对噪声具有一定的抑制作用,具有良好的泛化性能.

Kernel-based attribute reduction methods have shown great advantages for removing redundant information and adjusting nonlinear structure of input data.But in real applications,it is difficult for kernel-based attribute reduction methods to select optimal parameters.To do this,a new feature extraction method based on the optimization learning of adaptive kernel function was proposed in this paper.By use of improved Fisher kernel matrix measure criterion,an optimization framework of adaptive kernel function was established to deal with multiclassification task.Combining with optimization results,eigenvectors which make greater contribution to Renyi entropy estimation of input data were selected.New features were extracted based on selected eigenvectors in KECA feature subspace.Experimental results show that presented method can not only enhance the classification accuracy,but also restrain noise interference.