Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps
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...
| Հիմնական հեղինակներ: | , , , |
|---|---|
| Ձևաչափ: | Journal article |
| Հրապարակվել է: |
PNAS
2009
|
| Ամփոփում: | 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. |
|---|