  Diffusion maps are an emerging data-driven technique for non-linear
dimensionality reduction, which are especially useful for the analysis of
coherent structures and nonlinear embeddings of dynamical systems. However, the
computational complexity of the diffusion maps algorithm scales with the number
of observations. Thus, long time-series data presents a significant challenge
for fast and efficient embedding. We propose integrating the Nystr\"om method
with diffusion maps in order to ease the computational demand. We achieve a
speedup of roughly two to four times when approximating the dominant diffusion
map components.
