{
  "id": "1205.2629",
  "version": "v1",
  "published": "2012-05-09T17:14:10.000Z",
  "updated": "2012-05-09T17:14:10.000Z",
  "title": "Interpretation and Generalization of Score Matching",
  "authors": [
    "Siwei Lyu"
  ],
  "comment": "Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)",
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "Score matching is a recently developed parameter learning method that is particularly effective to complicated high dimensional density models with intractable partition functions. In this paper, we study two issues that have not been completely resolved for score matching. First, we provide a formal link between maximum likelihood and score matching. Our analysis shows that score matching finds model parameters that are more robust with noisy training data. Second, we develop a generalization of score matching. Based on this generalization, we further demonstrate an extension of score matching to models of discrete data.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2012-05-09T17:14:10.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "generalization",
      "interpretation",
      "score matching finds model parameters",
      "complicated high dimensional density models",
      "parameter learning method"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2012arXiv1205.2629L"
    }
  }
}