Casasent network density estimation

Density estimation is an integral component of many signal processing tasks such as pattern recognition, detection, and cluster analysis. The kernel density estimator´s utility is limited in high dimensional spaces by the curse of dimensionality. Modha (1994) proposed a neural network density estimator that avoids this `curse´, but can potentially require many hidden units (and therefore a lot of computation) for even simple densities such as Gaussians. We propose an extension of Modha´s neural net using higher order hidden units that allow simple densities to be modeled economically (as kernel estimators do), while retaining the flexibility to avoid the curse of dimensionality (as Modha´s network does)