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Warped Mixture Models for Meaningful Clustering and Bayesian Manifold Learning

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A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce meaningful 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 nonlinear clusters (or manifolds), whose number, shape and dimension is inferred automatically. We also discuss the pros and cons of Hamiltonian Monte Carlo versus variational inference in this nonparametric Bayesian model.

This talk is part of the Microsoft Research Machine Learning and Perception Seminars series.

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