将点云数据通过线性化最优传输嵌入，实现高效机器学习。
Linearized Optimal Transport pyLOT Library: A Toolkit for Machine Learning on Point Clouds
- 用固定参考分布的最优传输映射将概率分布线性化嵌入
- 在灵长类牙齿3D扫描上实现分类、聚类等任务的线性操作
- 适合点云分析与需要快速建模的机器学习场景
pyLOT库提供了线性化最优传输（LOT）技术的Python实现，支持下游机器学习任务。该方法通过固定参考分布的最优传输映射，将概率分布嵌入到希尔伯特空间中，实现线性化，使下游任务可使用现成的线性机器学习算法完成。我们以灵长类牙齿3D扫描为例，展示了分类、聚类、降维和数据生成等原始问题可简化为对LOT嵌入表示的简单线性操作。
原文摘要 · Abstract (English)
The pyLOT library offers a Python implementation of linearized optimal transport (LOT) techniques and methods to use in downstream tasks. The pipeline embeds probability distributions into a Hilbert space via the Optimal Transport maps from a fixed reference distribution, and this linearization allows downstream tasks to be completed using off the shelf (linear) machine learning algorithms. We provide a case study of performing ML on 3D scans of lemur teeth, where the original questions of classification, clustering, dimension reduction, and data generation reduce to simple linear operations performed on the LOT embedded representations.
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