Hilbert Space Embedding for Dirichlet Process Mixtures

Krikamol Muandet

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arXiv · 1210.4347

Hilbert Space Embedding for Dirichlet Process Mixtures

Krikamol Muandet

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Abstract

This paper proposes a Hilbert space embedding for Dirichlet Process mixture models via a stick-breaking construction of Sethuraman. Although Bayesian nonparametrics offers a powerful approach to construct a prior that avoids the need to specify the model size/complexity explicitly, an exact inference is often intractable. On the other hand, frequentist approaches such as kernel machines, which suffer from the model selection/comparison problems, often benefit from efficient learning algorithms. This paper discusses the possibility to combine the best of both worlds by using the Dirichlet Process mixture model as a case study.

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BibTeX RIS

Krikamol Muandet. 2012-10-16. Hilbert Space Embedding for Dirichlet Process Mixtures. https://arxiv.org/abs/1210.4347

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