{
  "id": "1603.02501",
  "version": "v1",
  "published": "2016-03-08T12:43:29.000Z",
  "updated": "2016-03-08T12:43:29.000Z",
  "title": "Mixture Proportion Estimation via Kernel Embedding of Distributions",
  "authors": [
    "Harish G. Ramaswamy",
    "Clayton Scott",
    "Ambuj Tewari"
  ],
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "Mixture proportion estimation (MPE) is the problem of estimating the weight of a component distribution in a mixture, given samples from the mixture and component. This problem constitutes a key part in many \"weakly supervised learning\" problems like learning with positive and unlabelled samples, learning with label noise, anomaly detection and crowdsourcing. While there have been several methods proposed to solve this problem, to the best of our knowledge no efficient algorithm with a proven convergence rate towards the true proportion exists for this problem. We fill this gap by constructing a provably correct algorithm for MPE, and derive convergence rates under certain assumptions on the distribution. Our method is based on embedding distributions onto an RKHS, and implementing it only requires solving a simple convex quadratic programming problem a few times. We run our algorithm on several standard classification datasets, and demonstrate that it performs comparably to or better than other algorithms on most datasets.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-03-08T12:43:29.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "mixture proportion estimation",
      "distribution",
      "kernel embedding",
      "simple convex quadratic programming problem",
      "proven convergence rate"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}