An Algorithm for Finding Intrinsic Dimensionality of Data

An algorithm for the analysis of multivariant data is presented along with some experimental results. The basic idea of the method is to examine the data in many small subregions, and from this determine the number of governing parameters, or intrinsic dimensionality. This intrinsic dimensionality is usually much lower than the dimensionality that is given by the standard Karhunen-Loève technique. An analysis that demonstrates the feasability of this approach is presented.