Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps

Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps

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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...

書誌詳細
主要な著者:	Singer, A, Erban, R, Kevrekidis, I, Coifman, R
フォーマット:	Journal article
出版事項:	PNAS 2009

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