Data classification, where the goal is to divide data into predefined classes, is a fundamental problem in machine learning with many applications, including the classification of 3D sensory data. In this paper, we present a data classification method which can be applied to both semi-supervised and unsupervised learning tasks. The algorithm is derived by unifying complementary region-based and edge-based approaches; a gradient flow of the optimization energy is performed using modified auction dynamics. In addition to being unconditionally stable and efficient, the method is equipped with several properties allowing it to perform accurately even with small labeled training sets, often with considerably fewer labeled training elements compared to competing methods; this is an important advantage due to the scarcity of labeled training data. Some of the properties are: the embedding of data into a weighted similarity graph, the in-depth construction of the weights using, e.g., geometric information, the use of a combination of region-based and edge-based techniques, the incorporation of class size information and integration of random fluctuations. The effectiveness of the method is demonstrated by experiments on classification of 3D point clouds; the algorithm classifies a point cloud of more than a million points in 1–2 min.

目标是将数据划分为预定义的类别的数据分类是机器学习在许多应用程序中的一个基本问题，包括3D感官数据的分类。在本文中，我们提出了一种数据分类方法，该方法可以应用于半监督和无监督学习任务。该算法是通过统一互补的基于区域和基于边缘的方法而得出的。使用修改后的拍卖动力学执行优化能量的梯度流。除了无条件地稳定和高效之外，该方法还具有多种特性，即使在使用小型标记训练集的情况下也能准确执行，与竞争方法相比，标记训练元素通常要少得多；由于缺少标记的训练数据，这是一个重要的优势。一些特性是：将数据嵌入到加权相似度图中；使用例如几何信息进行权重的深入构造；结合使用基于区域和基于边缘的技术；将类别合并尺寸信息和随机波动的整合。通过对3D点云分类的实验证明了该方法的有效性。该算法在1-2分钟内将超过一百万个点的点云分类。通过对3D点云分类的实验证明了该方法的有效性。该算法在1-2分钟内将超过一百万个点的点云分类。通过对3D点云分类的实验证明了该方法的有效性。该算法在1-2分钟内将超过一百万个点的点云分类。