Evaluating dimensionality reduction?
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Description
Hello Leland,
Thank you for sharing this new algorithm.
I have a question regarding evaluation measures of dimensionality reduction methods. I'm aware of trustworthiness and continuity, but I'm looking for measures that can handle large datasets.
I found the paper "[Scale-independent quality criteria for dimensionality reduction](https://perso.uclouvain.be/michel.verleysen/papers/patreclet10jl.pdf)" which is an alternative quality measure, but it is still for small datasets.
How are you evaluating umap against other approaches at the moment?
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Research direction
No files, tests, or entry points are named. First clarify whether this is seeking an implementation, documentation, or only a benchmark comparison, then define the large-dataset evaluation measures and comparison scope. Done would require an agreed evaluation approach and documented results.
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Assessment
- Tech stack
- python
- Domain
- data-visualization, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100