UMAP and Locality Weighting
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Description
In the docs you say
> And our results in terms of adjusted Rand score and adjusted mutual information are in line with the current state of the art techniques using convolutional autoencoder techniques. That’s not bad for an approach that is simply viewing the data as arbitrary 784 dimensional vectors.
In your opinion, would it be possible to add a concept of "locality" to UMAP? So for an image, you could give more weight to features (pixels) that are "spatially" close to each other
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the documentation passage quoted in the issue and review how UMAP currently handles image data represented as 784-dimensional vectors. Define whether spatially local pixel weighting should be supported and what evaluation would demonstrate that it works; the issue does not name implementation files or tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100