{
  "id": "1210.4347",
  "version": "v1",
  "published": "2012-10-16T10:26:29.000Z",
  "updated": "2012-10-16T10:26:29.000Z",
  "title": "Hilbert Space Embedding for Dirichlet Process Mixtures",
  "authors": [
    "Krikamol Muandet"
  ],
  "comment": "NIPS 2012 Workshop in confluence between kernel methods and graphical models",
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2012-10-16T10:26:29.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "hilbert space embedding",
      "dirichlet process mixture model",
      "model selection/comparison problems",
      "bayesian nonparametrics offers",
      "exact inference"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2012arXiv1210.4347M"
    }
  }
}