1. A method for dimensionality reduction of large data volumes comprising the steps of: a. providing a dataset Γ of data points given as vectors; b. building a weighted graph G on Γ with a weight function w ε , wherein w ε corresponds to a local coordinate-wise similarity between the coordinates in Γ; c. constructing a random walk on graph G via a Markov transition matrix P, which is derived from w ε ; d. performing a spectral decomposition of P to obtain right and left eigenvectors of P; and e. projecting the data points in Γ onto the right eigenvectors of P to obtain a set of projection values Γ B for each data point, whereby Γ B represents coordinates in a reduced space.