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[FEA] PaCMAP manifold dimension reduction
Is your feature request related to a problem? Please describe.
I would like to have a GPU-accelerated version of the PaCMAP algorithm, a competitor to UMAP. Personally, I would use this with the BERTopic topic-modeling framework but PaCMAP could in theory be used anywhere that algorithms like tSNE or UMAP are used.
Describe the solution you'd like
Similar to cuML.manifold.umap, it would be a python implementation of PaCMAP that runs on GPU.
Additional context
- The PaCMAP GitHub repo is here: https://github.com/YingfanWang/PaCMAP
- The paper is here (JMLR 2021): https://jmlr.org/papers/v22/20-1061.html
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I would also like to see this. I find PaCMAP to generate more useable embeddings, faster and with less parameter optimization.
This issue has been labeled inactive-30d
due to no recent activity in the past 30 days. Please close this issue if no further response or action is needed. Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed. This issue will be labeled inactive-90d
if there is no activity in the next 60 days.
up 👍
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