Graph-based normalization and whitening for non-linear data analysis
In this paper we construct a graph-based normalization algorithm for non-linear data analysis. The principle of this algorithm is to get a spherical average neighborhood with unit radius. First we present a class of global dispersion measures used for global normalization; we then adapt these measures using a weighted graph to build a local normalization called graph-based normalization. Then we give details of the graph-based normalization algorithm and illustrate some results. In the second part we present a graph-based whitening algorithm built by analogy between the global and the local problem.
graph normalization geodesic distance som