Likelihood Landscape and Local Minima Structures of Gaussian Mixture Models
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In this paper, we study the landscape of the population negative log-likelihood function of Gaussian Mixture Models with a general number of components. Due to nonconvexity, there exist multiple local minima that are not globally optimal, even when the mixture is well-separated. We show that all local minima share the same form of structure that partially identifies the component centers of the true mixture, in the sense that each local minimum involves a non-overlapping combination of fitting multiple Gaussians to a single true component and fitting a single Gaussian to multiple true components. Our results apply to the setting where the true mixture components satisfy a certain separation condition, and are valid even when the number of components is over-or under-specified. For Gaussian mixtures with three components, we obtain sharper results in terms of the scaling with the separation between the components.
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