Gauss mixtures have gained popularity in statistics and statistical signal processing applications for a variety of reasons, including their ability to well approximate a large class of interesting densities and the availability of algorithms such as EM for constructing the models based on observed data. We here consider a different motivation and framework based on the information theoretic view of Gaussian sources as a "worst case" for compression developed by Sakrison and Lapidoth. This provides an approach for clustering Gauss mixture models using a minimum discrimination distortion measure and provides the intuitive support that good modeling is equivalent to good compression.
W e present a technique t o speed u p classification of document images such as web pages. T h e technique involves performing all computation off-line and building the results into tables, so that during actual classification the only operations performed are simple table-lookups. Results show that the probability of misclassification f o r table-lookup gives good performance and the implementation is m u c h faster.
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