Abstract :
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.
Keywords :
Data reduction, dimensionality reduction, interactive systems, intrinsic dimensionality, Karhunen-Loève expansion, multivariant data analysis, principal component, stochastic processes.; Algorithm design and analysis; Data analysis; Multidimensional systems; Principal component analysis; Random processes; Random variables; Statistical distributions; Stochastic processes; Testing; Data reduction, dimensionality reduction, interactive systems, intrinsic dimensionality, Karhunen-Loève expansion, multivariant data analysis, principal component, stochastic processes.;
