KPCA与LTSA融合的转子故障数据集降维算法

针对核主成分分析(kernel principal component analysis,KPCA)和局部切空间排列算法(local tangent space,LTSA)在降维过程中无法兼顾保持数据全局结构特性和局部结构特性的问题,利用核函数的可线性叠加性质,提出一种将KPCA算法与LTSA算法融合的非线性降维算法....

Research on the method of dimension reduction of rotor fault data set by fusing KPCA and LTSA

In order to solve the problem that kernel principal component analysis (KPCA) and local tangent space alignment (LTSA) algorithm can not keep both the global and local structure characteristics of data in the process of dimensionality reduction, this paper uses linear superposition of kernel function to derive a nonlinear dimensionality reduction algorithm which combines KPCA algorithm with LTSA algorithm. The dimensionality reduction algorithm can make a fault dataset maintaining both global distance relationship and local neighborhood relationship between data samples after dimensionality reduction. Computational experiments show that this algorithm can accurately extract the global and local structural characteristics contained in the fault dataset, and make the results of fault classification clearer and more accurate as well as more effective.