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Title:On the Number of Modes of a Gaussian Mixture
Authors: Miguel Carreira-Perpinan ; Chris Williams
Date:Feb 2003
We consider a problem intimately related to the creation of maxima under Gaussian blurring: the number of modes of a Gaussian mixture in D dimensions. To our knowledge, a general answer to this question is not known. We conjecture that if the components of the mixture have the same covariance matrix (or the same covariance matrix up to a scaling factor), then the number of modes cannot exceed the number of components. We demonstrate that the number of modes can exceed the number of components when the components are allowed to have arbitrary and different covariance matrices. We will review related results from scale-space theory, statistics and machine learning, including a proof of the conjecture in 1D. We present a convergent, EM-like algorithm for mode finding and compare results of searching for all modes starting from the centers of the mixture components with a brute-force search. We also discuss applications to data reconstruction and clustering.
2003 by The University of Edinburgh. All Rights Reserved
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Bibtex format
author = { Miguel Carreira-Perpinan and Chris Williams },
title = {On the Number of Modes of a Gaussian Mixture},
year = 2003,
month = {Feb},

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