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Mode-finding for mixtures of Gaussian distributions

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1 Author(s)
M. A. Carreira-Perpinan ; Dept. of Neurosci., Georgetown Univ. Med. Center, Washington, DC, USA

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.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:22 ,  Issue: 11 )