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Mixtures of Gaussian Wells: Theory, Computation, and Application.

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.

SUBMITTER: Manolopoulou I

PROVIDER: S-EPMC3384503 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Mixtures of Gaussian Wells: Theory, Computation, and Application.

Manolopoulou Ioanna I Kepler Thomas B TB Merl Daniel M DM

Computational statistics & data analysis 20120521 12

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 p ...[more]

PMID: 22754052

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Dataset Information

Mixtures of Gaussian Wells: Theory, Computation, and Application.

Publications

Mixtures of Gaussian Wells: Theory, Computation, and Application.

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