A Fast Graph-Based Data Classification Method with Applications to 3D Sensory Data in the Form of Point Clouds
Monday, September 14, 2020 - 1:40pm - 2:25pm
Ekaterina Rapinchuk (Michigan State University)
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 minutes.
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 minutes.