# Linearized Optimal Transport pyLOT Library: A Toolkit for Machine Learning on Point Clouds

arXiv: https://arxiv.org/abs/2502.03439 · HTML: https://mlsanity.com/paper/2502.03439/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2025.

## Built on by (most cited first, 1 of 1)

- [Linearized Optimal Transport for Analysis of High-Dimensional Point-Cloud and Single-Cell Data](https://mlsanity.com/paper/2510.22033/) (2025)

## Abstract (arXiv)

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.