Mixtures of Gaussian wells: Theory, computation, and application

Ioanna Manolopoulou, Thomas B. Kepler and Daniel M. Merl

Computational Statistics & Data Analysis, 2012, vol. 56, issue 12, 3809-3820

Abstract: A primary challenge in unsupervised clustering using mixture models is the selection of a family of basis distributions flexible enough to succinctly represent the distributions of the target subpopulations. In this paper we introduce a new family of Gaussian well distributions (GWDs) for clustering applications where the target subpopulations are characterized by hollow (hyper-)elliptical structures. We develop the primary theory pertaining to the GWD, including mixtures of GWDs, selection of prior distributions, and computationally efficient inference strategies using Markov chain Monte Carlo. We demonstrate the utility of our approach, as compared to standard Gaussian mixture methods on a synthetic dataset, and exemplify its applicability on an example from immunofluorescence imaging, emphasizing the improved interpretability and parsimony of the GWD-based model.

Keywords: Gaussian mixtures; Poisson point processes; Subtractive mixtures; Histology (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:12:p:3809-3820

DOI: 10.1016/j.csda.2012.03.027

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