Before revision: Here I introduce an extension to demixed principal component analysis (dPCA), a  linear dimensionality reduction technique for analyzing the activity of neural populations, to the case of nonlinear components . This extension, kernel demixed principal component analysis (kdPCA) , relies on kernel least-squares regression techniques, and it resembles kernel-based extensions to standard principal component analysis and canonical correlation analysis. kdPCA includes dPCA as a special case when the kernel is linear.
After revision: Here I introduce an extension to demixed principal component analysis (dPCA), a linear dimensionality reduction technique to find components that depend on particular experimental parameters. Here, I introduce kernel dPCA (kdPCA) , relies on kernel least-squares regression techniques, and it resembles kernel-based extensions to standard principal component analysis and canonical correlation analysis. kdPCA includes dPCA as a special case when the kernel is linear.