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

C. Levasseur, K. Kreutz-Delgado, U. Mayer, G. Gancarz
Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005.  
Many important expert system 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 by applying generalized principal component analysis (GPCA) techniques for pattern extraction based on an exponential family
more » ... al 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.
doi:10.1109/acssc.2005.1599808 fatcat:n6nmu2hp6jdshjdqhonsxa5sgi