Uncertainty in Artificial Intelligence
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Warped Mixtures for Nonparametric Cluster Shapes
Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani
Abstract:
A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allows us to summarize the properties of the high-dimensional clusters (or density manifolds) describing the data. The number of manifolds, as well as the shape and dimension of each manifold is automatically inferred. We derive a simple inference scheme for this model which analytically integrates out both the mixture parameters and the warping function. We show that our model is effective for density estimation, performs better than infinite Gaussian mixture models at recovering the true number of clusters, and produces interpretable summaries of high-dimensional datasets.
Keywords:
Pages: 311-320
PS Link:
PDF Link: /papers/13/p311-iwata.pdf
BibTex:
@INPROCEEDINGS{Iwata13,
AUTHOR = "Tomoharu Iwata and David Duvenaud and Zoubin Ghahramani",
TITLE = "Warped Mixtures for Nonparametric Cluster Shapes",
BOOKTITLE = "Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2013",
PAGES = "311--320"
}


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