Visualize non-Euclidean embeddings about nomic HOT 3 CLOSED

There is not currently a way to specify a custom distance function and there most likely won't be one in the short term--but it couldn't hurt to just try the high-d hyperbolic space and see if it looks OK?

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I tried visualizing 128 dimensional points on the Poincare model of hyperbolic geometry. The data possesses some structure but not a ton. It is unclear if this is because the dimensionality reduction violates the assumptions of the manifold or if the data is just inherently noisy. So to answer your question, the data does look OK but that is not entirely satisfactory for me.

Also, I just wanted to clarify: does the dimensionality reduction algorithm assume we are in Euclidean space? Or is it just the nearest neighbor search which assumes we are in Euclidean space?

Thanks!

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Yes, the dimensionality reduction assumes a kernel that is Euclidean

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