Data-Pattern Discovery Methods for Detection in Nongaussian High-dimensional Data Sets

Many important analytic applications depend on the ability to accurately detect or predict the occurrence of key events given a data set of observations. We concentrate on multidimensional data that are highly nonGaussian (continuous and/or discrete), noisy and nonlinearly related. We investigate the feasibility of data-pattern discovery and event detection in such domains by applying generalized principal component analysis (GPCA) techniques for pattern extraction based on an exponential family probability distribution assumption. We develop theoretical extensions of the GPCA model by exploiting results from the theory of generalized linear models and nonparametric mixture density estimation