Introduction - If you have any usage issues, please Google them yourself
In the field of multivariate statistics, kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using kernel function technology. The linear operation of the original PCA is performed in a replicated kernel Hilbert space using a kernel function. The operation step potential of KPCA is to transform the data into kernels before PCA, and then calculate the correlation coefficient matrix.