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You can't visualize it but you can certainly compute the euclidean distance. Tools like UMAP can be used to drop the dimensionality as well.
by osigurdson 1y ago
You can't visualize it but you can certainly compute the euclidean distance. Tools like UMAP can be used to drop the dimensionality as well.
- aswanson 1y agoAny good umap links?
- minimaxir 1y agoFor small datasets, the original UMAP package is fine: https://umap-learn.readthedocs.io/en/latest/ https://umap-learn.readthedocs.io/en/latest/ For large datasets (as the UMAP algorithm scales in exponential compute), you will need to use the GPU-accelerated UMAP from cuML. https://docs.rapids.ai/api/cuml/stable/api/#umap https://docs.rapids.ai/api/cuml/stable/api/#umap
- minimaxir 1y agoSpeaking of UMAP, a new update to the cuML library (https://github.com/rapidsai/cuml https://github.com/rapidsai/cuml) released last month allows UMAP to feasibly be used on big data without shenanigans/spending a lot of money. This opens up quite a few new oppertunities and I'm getting very good results with.