Supervised Learning of Labeled Pointcloud Differences via Cover-Tree Entropy Reduction
This work addresses a domain-specific problem in machine learning for pointcloud data, presenting an incremental method that combines existing techniques.
The paper tackles the problem of supervised learning on labeled pointclouds by introducing CDER, an algorithm that merges Cover Trees with entropy reduction to identify differences between pointclouds at various scales, achieving linear time complexity in typical cases.
We introduce a new algorithm, called CDER, for supervised machine learning that merges the multi-scale geometric properties of Cover Trees with the information-theoretic properties of entropy. CDER applies to a training set of labeled pointclouds embedded in a common Euclidean space. If typical pointclouds corresponding to distinct labels tend to differ at any scale in any sub-region, CDER can identify these differences in (typically) linear time, creating a set of distributional coordinates which act as a feature extraction mechanism for supervised learning. We describe theoretical properties and implementation details of CDER, and illustrate its benefits on several synthetic examples.