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Abstract:
The marriage of Renyi entropy with Parzen density estimation
has been shown to be a viable tool in learning discriminative
feature transforms. However, it suffers from computational
complexity proportional to the square of the number of samples in
the training data. This sets a practical limit to using large
databases. We suggest immediate divorce of the two methods and
remarriage of Renyi entropy with a semi-parametric density
estimation method, such as a Gaussian Mixture Models (GMM). This
allows all of the computation to take place in the low
dimensional target space, and it reduces computational complexity
proportional to square of the number of components in the
mixtures. Furthermore, a convenient extension to Hidden Markov
Models as commonly used in speech recognition becomes
possible.
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