Detecting Intrinsic Slow Variables in Stochastic Dynamical Systems by Anisotropic Diffusion maps

摘要

Nonlinear independent component analysis is combined with diffusion- map data analysis techniques to detect good observables in high-dimensional dynamic data. These detections are achieved by integrating local principal component analysis of simulation bursts by using eigenvectors of a Markov matrix describing anisotropic diffusion. The widely applicable procedure, a crucial step in model reduction approaches, is illustrated on stochastic chemical reaction network simulations.