{
  "id": "2006.15791",
  "version": "v1",
  "published": "2020-06-29T03:21:38.000Z",
  "updated": "2020-06-29T03:21:38.000Z",
  "title": "Probabilistic Classification Vector Machine for Multi-Class Classification",
  "authors": [
    "Shengfei Lyu",
    "Xing Tian",
    "Yang Li",
    "Bingbing Jiang",
    "Huanhuan Chen"
  ],
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "The probabilistic classification vector machine (PCVM) synthesizes the advantages of both the support vector machine and the relevant vector machine, delivering a sparse Bayesian solution to classification problems. However, the PCVM is currently only applicable to binary cases. Extending the PCVM to multi-class cases via heuristic voting strategies such as one-vs-rest or one-vs-one often results in a dilemma where classifiers make contradictory predictions, and those strategies might lose the benefits of probabilistic outputs. To overcome this problem, we extend the PCVM and propose a multi-class probabilistic classification vector machine (mPCVM). Two learning algorithms, i.e., one top-down algorithm and one bottom-up algorithm, have been implemented in the mPCVM. The top-down algorithm obtains the maximum a posteriori (MAP) point estimates of the parameters based on an expectation-maximization algorithm, and the bottom-up algorithm is an incremental paradigm by maximizing the marginal likelihood. The superior performance of the mPCVMs, especially when the investigated problem has a large number of classes, is extensively evaluated on synthetic and benchmark data sets.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-06-29T03:21:38.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "multi-class classification",
      "multi-class probabilistic classification vector machine",
      "bottom-up algorithm",
      "top-down algorithm",
      "sparse bayesian solution"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}