@article{38846,
  keywords = {reduction, slow manifold, geometric diffusions, harmonic-analysis, representation, equation, structure definition, dimensionality reduction, tool, chemical reactions},
  author = {Amit Singer and Radek Erban and Ioannis Kevrekidis and Ronald Coifman},
  title = {Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps},
  abstract = { <p>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.</p> },
  year = {2009},
  journal = {Proceedings of the National Academy of Sciences of the United States of America},
  volume = {106},
  pages = {16090-16095},
  month = {09/2009},
  isbn = {0027-8424},
  language = {English},
}