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Abstract: We present a method for learning discriminative feature transforms using as criterion the mutual information between class labels and transformed features. Instead of a commonly used mutual information measure based on Kullback-Leibler divergence, we use a quadratic divergence measure, which allows us to make an efficient non-parametric implementation and requires no prior assumptions about class densities. In addition to linear transforms, we also discuss nonlinear transforms that are... (Update)
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BibTeX entry: (Update)
@misc{ torkkola-journal,
author = "Maximization Kari Torkkola",
title = "Journal of Machine Learning Research 3 (2003) 1415-1438 Submitted 5/02;
Published 3/03 Feature Extraction by Non-Parametric Mutual Information",
url = "citeseer.ist.psu.edu/766099.html" }
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