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Warped Mixture Models for Meaningful Clustering and Bayesian Manifold Learning

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David Duvenaud, University of Cambridge
Monday 11 March 2013, 14:00-15:00
Auditorium, Microsoft Research Ltd, 21 Station Road, Cambridge, CB1 2FB.

If you have a question about this talk, please contact Microsoft Research Cambridge Talks Admins .

This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending

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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Warped Mixture Models for Meaningful Clustering and Bayesian Manifold Learning

📅 Download to calendar (vCal)

⚠️ Important: This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending

👤 Speaker: David Duvenaud, University of Cambridge
📅 Date & Time: Monday 11 March 2013, 14:00 - 15:00
📍 Venue: Auditorium, Microsoft Research Ltd, 21 Station Road, Cambridge, CB1 2FB

Questions? Contact Microsoft Research Cambridge Talks Admins

Abstract

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

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Warped Mixture Models for Meaningful Clustering and Bayesian Manifold Learning

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Warped Mixture Models for Meaningful Clustering and Bayesian Manifold Learning

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Warped Mixture Models for Meaningful Clustering and Bayesian Manifold Learning

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