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ABSTRACT
<p><b>Abstract</b>—Gradient-quadratic and fixed-point iteration algorithms and appropriate values for their control parameters are derived for finding all modes of a Gaussian mixture, a problem with applications in clustering and regression. The significance of the modes found is quantified locally by Hessian-based error bars and globally by the entropy as sparseness measure.</p>
INDEX TERMS
Gaussian mixtures, maximization algorithms, mode finding, bump finding, error bars, sparseness.
CITATION
Miguel Á. Carreira-Perpiñán, "Mode-Finding for Mixtures of Gaussian Distributions", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 22, no. , pp. 1318-1323, November 2000, doi:10.1109/34.888716
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