Learning Discriminative Feature Transforms to Low Dimensions in Low Dimentions

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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Kari Torkkola


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 al- lows 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 conve- nient extension to Hidden Markov Models as commonly used in speech recognition becomes possible.