UMAP: a faster map of high-dimensional data (and how it beats t-SNE)
Article summary
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Yesterday I built t-SNE from scratch and watched six-dimensional blobs untangle into a clean 2D picture. It works, and the clusters it draws are gorgeous. But two things nagged me. It was slow — every iteration touched all N-squared pairs of points. And it threw away the big picture: the distance between two clusters on a t-SNE plot means nothing at all. So today I built its successor, UMAP, and it fixes both problems while keeping the crisp local clusters. The mental shift: from probabilities…
1Key Takeaways
- Yesterday I built t-SNE from scratch and watched six-dimensional blobs untangle into a clean 2D picture.
- It works, and the clusters it draws are gorgeous.
- It was slow — every iteration touched all N-squared pairs of points.
- And it threw away the big picture: the distance between two clusters on a t-SNE plot means nothing at all.
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that yesterday I built t-SNE from scratch and watched six-dimensional blobs untangle into a clean 2D picture.
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