UMAP Dimension Reduction, Main Ideas!!!

UMAP is one of the most popular dimension-reductions algorithms and this StatQuest walks you through UMAP, one step at a time, so that you will have a solid understanding of how UMAP works.

NOTE: This StatQuest is based on the original UMAP manuscript...
https://arxiv.org/pdf/1802.03426.pdf
...specifically Appendix C, From t-SNE to UMAP, which is also here...
https://jlmelville.github.io/uwot/uma...
...and the UMAP user documentation...
https://umap-learn.readthedocs.io/en/...

For a complete index of all the StatQuest videos, check out...
https://app.learney.me/maps/StatQuest
...or...
https://statquest.org/video-index/

If you'd like to support StatQuest, please consider...

Buying my book, The StatQuest Illustrated Guide to Machine Learning:
PDF - https://statquest.gumroad.com/l/wvtmc
Paperback - https://www.amazon.com/dp/B09ZCKR4H6
Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC

Patreon:   / statquest  
...or...
YouTube Membership:    / @statquest  

...a cool StatQuest t-shirt or sweatshirt:
https://shop.spreadshirt.com/statques...

...buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/

...or just donating to StatQuest!
https://www.paypal.me/statquest

Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
  / joshuastarmer  

0:00 Awesome song and introduction
1:07 Motivation for UMAP
2:55 UMAP main ideas
5:22 Calculating high-dimensional similarity scores
10:41 Getting started with the low-dimensional graph
12:37 Calculating low-dimensional similarity scores and moving points
15:49 UMAP vs t-SNE

#StatQuest #UMAP #DimensionReduction