Nonlinear demixed component analysis for neural population data as a low-rank kernel regression problem
Kenneth W. Latimer
Abstract: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. … Show more
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