How to compute hyperbolic embeddings for leafs of an existing tree

[vadimkantorov]

Hi!

Before doing more flexible learning, I'd like to learn hyperbolic embeddings for leaves of an existing tree (a tree from image region merging agglomerative clustering procedure). Such embeddings that when applying the tree decoding algorithm from HypHC, it would give back my original agglomerative clustering tree .

Can I do it within the framework / losses of HypHC?

I thought of using the tree shortest path for the similarity matrix.

The tricky part is that the similarity matrix obtained this way is extremely sparse, so randomly sampled triplets almost always have 0 similarities, so the model learns nothing.

Would you have any advices?

Thank you :)