Sounds like “Non-Euclidean” is the same thing as “non-spacial data or spatial data in more than 2 dimensions.” Is that accurate?

Also, I’m a little confused by the discussion about reducing the dimensionality of the data. You talk about 3D data being projected to 2D approximation and how that loses information. But when you talk about 2D data wouldn’t the correct anaology be projecting it to a 1D approximation?

## What Is Geometric Deep Learning

Thank you for this detailed explanation

edited Jun 12, 2022Sounds like “Non-Euclidean” is the same thing as “non-spacial data or spatial data in more than 2 dimensions.” Is that accurate?

Also, I’m a little confused by the discussion about reducing the dimensionality of the data. You talk about 3D data being projected to 2D approximation and how that loses information. But when you talk about 2D data wouldn’t the correct anaology be projecting it to a 1D approximation?