Abstract: Methods for dimensionality reduction of large data volumes, in particular hyper-spectral data cubes, include providing a dataset ? of data points given as vectors, building a weighted graph G on ? with a weight function w?, wherein w? corresponds to a local coordinate-wise similarity between the coordinates in ?; obtaining eigenvectors of a matrix derived from graph G and weight function w?, and projecting the data points in ? onto the eigenvectors to obtain a set of projection values ?B for each data point, whereby ?B represents coordinates in a reduced space. In one embodiment, the matrix is constructed through the dividing each element of w? by a square sum of its row multiplied by a square sum of its column. In another embodiment the matrix is constructed through a random walk on graph G via a Markov transition matrix P, which is derived from w?. The reduced space coordinates are advantageously used to rapidly and efficiently perform segmentation and clustering.

Abstract: Methods for dimensionality reduction of large data volumes, in particular hyper-spectral data cubes, include providing a dataset ? of data points given as vectors, building a weighted graph G on ? with a weight function w?, wherein w? corresponds to a local coordinate-wise similarity between the coordinates in ?; obtaining eigenvectors of a matrix derived from graph G and weight function w?, and projecting the data points in ? onto the eigenvectors to obtain a set of projection values ?B for each data point, whereby ?B represents coordinates in a reduced space. In one embodiment, the matrix is constructed through the dividing each element of w? by a square sum of its row multiplied by a square sum of its column. In another embodiment the matrix is constructed through a random walk on graph G via a Markov transition matrix P, which is derived from w?. The reduced space coordinates are advantageously used to rapidly and efficiently perform segmentation and clustering.