{
  "id": "2002.03281",
  "version": "v1",
  "published": "2020-02-09T04:49:32.000Z",
  "updated": "2020-02-09T04:49:32.000Z",
  "title": "PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification",
  "authors": [
    "Min Zhang",
    "Yifan Wang",
    "Pranav Kadam",
    "Shan Liu",
    "C. -C. Jay Kuo"
  ],
  "comment": "4pages, 4 figures",
  "categories": [
    "cs.CV",
    "cs.LG"
  ],
  "abstract": "The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction. It has an extremely low training complexity while achieving state-of-the-art classification performance. In this work, we improve the PointHop method furthermore in two aspects: 1) reducing its model complexity in terms of the model parameter number and 2) ordering discriminant features automatically based on the cross-entropy criterion. The resulting method is called PointHop++. The first improvement is essential for wearable and mobile computing while the second improvement bridges statistics-based and optimization-based machine learning methodologies. With experiments conducted on the ModelNet40 benchmark dataset, we show that the PointHop++ method performs on par with deep neural network (DNN) solutions and surpasses other unsupervised feature extraction methods.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-02-09T04:49:32.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "lightweight learning model",
      "point sets",
      "3d classification",
      "pointhop method",
      "3d point cloud classification"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 4,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}