  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.
