Cartan-Schouten metrics for information geometry and machine learning
Andre Diatta, Bakary Manga, Fatimata Sy

TL;DR
This paper introduces Cartan-Schouten metrics for Lie groups, exploring their properties, computational advantages, and applications in data science and machine learning, including a new parametric mean for data analysis.
Contribution
It characterizes Cartan-Schouten metrics, provides explicit formulas for their computation, and demonstrates their advantages over traditional metrics in machine learning contexts.
Findings
Cartan-Schouten metrics are determined by their value at the unit.
They are abundant on 2-nilpotent Lie groups.
These metrics enable a new parametric mean for data science.
Abstract
We study Cartan-Schouten metrics, explore invariant dual connections, and propose them as models for Information Geometry. Based on the underlying Riemannian barycenter and the biinvariant mean of Lie groups, we subsequently propose a new parametric mean for data science and machine learning which comes with several advantages compared to traditional tools such as the arithmetic mean, median, mode, expectation, least square method, maximum likelihood, linear regression. We call a metric on a Lie group, a Cartan-Schouten metric, if its Levi-Civita connection is biinvariant, so every 1-parameter subgroup through the unit is a geodesic. Except for not being left or right invariant in general, Cartan-Schouten metrics enjoy the same geometry as biinvariant metrics, since they share the same Levi-Civita connection. To bypass the non-invariance apparent drawback, we show that…
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Taxonomy
TopicsTopological and Geometric Data Analysis
