Learning Discriminative Feature Transforms to Low Dimensions in Low Dimentions

@inproceedings{Torkkola2001LearningDF,
  title={Learning Discriminative Feature Transforms to Low Dimensions in Low Dimentions},
  author={Kari Torkkola},
  booktitle={NIPS},
  year={2001}
}
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… CONTINUE READING
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