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

Cécile Levasseur, Ken Kreutz-Delgado, Uwe F. Mayer and Gregory Gancarz

Abstract: 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 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.

Key words: Principal component analysis, exponential family probability distribution, generalized linear models.

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