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Tropical geometric tools for machine learning: the TML package

David Barnhill, Ruriko Yoshida, Georgios Aliatimis and Keiji Miura

Vol. 14 (2024), 133–174
Abstract

In the last decade, developments in tropical geometry have provided a number of uses directly applicable to problems in statistical learning. The TML package is the first R package which contains a comprehensive set of tools and methods used for basic computations related to tropical convexity, visualization of tropically convex sets, as well as supervised and unsupervised learning models using the tropical metric under the max-plus algebra over the tropical projective torus. Primarily, the TML package employs a Hit-and-Run Markov chain Monte Carlo sampler in conjunction with the tropical metric as its main tool for statistical inference. In addition to basic computation and various applications of the tropical HAR sampler, we also focus on several supervised and unsupervised methods incorporated in the TML package including tropical principal component analysis, tropical logistic regression and tropical kernel density estimation.

Keywords
tropical machine learning, tropical geometry, tropical data science
Mathematical Subject Classification
Primary: 14T90, 62G09, 62R01
Supplementary material

Tropical machine learning methods and functionality in R.

Milestones
Received: 24 September 2023
Revised: 1 June 2024
Accepted: 16 July 2024
Published: 5 October 2024
Authors
David Barnhill
Mathematics Department
US Naval Academy
Annapolis, MD
United States
Ruriko Yoshida
Department of Operations Research
Naval Postgraduate School
Monterey, CA
United States
Georgios Aliatimis
STOR-i Centre for Doctoral Training
Lancaster University
Lancaster
United Kingdom
Keiji Miura
Kwansei Gakuin University
Sanda
Japan