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Machine Learning in a Non-Euclidean Space
Latest   Machine Learning

Machine Learning in a Non-Euclidean Space

Last Updated on July 24, 2023 by Editorial Team

Author(s): Mastafa Foufa

Originally published on Towards AI.

Chapter II. How to get an intuition about hyperbolic geometry and when to use it in your Data Science projects?

Photo by Mae Mu on Unsplash

1. There are different examples of non-Euclidean geometry, among them spherical geometry and hyperbolic geometry.

2. A hyperbolic space is a space of negative constant curvature.

3. There are different models of hyperbolic geometry, the most famous being the Poincaré ball model.

4. For datasets with a hierarchical structure, it is better to represent it in a hyperbolic space, because both a hyperbolic space and a hierarchical dataset have an inherent exponential growth.

M: Aniss, could you give us some intuition behind hyperbolic geometry and allow us to understand for what kind of data it is relevant? I know… Read the full blog for free on Medium.

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