UMAP Dimension Reduction, Main Ideas!!!

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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...
...specifically Appendix C, From t-SNE to UMAP, which is also here...
...and the UMAP user documentation...

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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
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This is such perfect timing, I'm supposed to learn and perform a UMAP reduction tomorrow. Thank you!

EthanSalter
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After three days of coming back to this video, I think I finally got it... Thanks Josh. When I'm in a place to support, I will

codewithbrogs
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I just found this channel. I'm currently doing my PhD in Bioinformatics and this is helping me immensely to save a lot of time and to learn new methods faster and better (I have a graphical brain so :/) Thank you so much for this!!

evatosco-herrera
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I can't appreciate how much this channel helped me - so clearly explained!!

aiexplainai
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I really appreciated the UMAP vs t-SNE part. Thanks for the video! Really helpful when one tries to get the main idea behind all the math :)

terezamiklosova
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Dude... Dude... You have a gift for explaining stats. Superb.

kennethm.
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This is awesome, thanks for explaining UMAP so well, and clearly explaining when to use! Love the topics you’re covering

markmalkowski
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Great Video, Thank you! You are with me since first semester and I am so happy to see a video by you on a topic that is relevant to me

offswitcher
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Wowie, I can finally learn what UMAP stands for and how it reduces dimensionality AFTER I analysed my scRNA-seq data with it's help!

abramcadabros
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New StatQuest always gets me amped. High yield, low drag material!!!

JulietNovember
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Not sure if I can hold my breath for long enough before the video starts, Amazing work!! @StatQuest

akashkewar
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I was waiting for this. thank you. best dimensionally reduced visual explanation out there.

dexterdev
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I'd love to see a cross-over episode between StatQuest and Casually Explained.

Big bada-bam.

VCC
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This will help me greatly for my MS project.

hgjkq
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Hello Josh, thank you so much for the amazing video! I have a question about the mapping consistency of UMAP.
In the video, UMAP can keep mapping consistency (meaning that the mapping does not change over the iteration) when we map the projected points on low-dimensional plane based on high-dimensional similarity score, unlike to t-SNE. My question is, it doesn't necessarily mean the final visualization result would be consistent for all time, right? Because since there is randomized sampling, I don't think the final result would be consistent. I tried it using umap-learn lib and the result was also inconsistent.
I'm not sure I explained well on my question but please feel free to tell me if there's any ambiguous points. Thank you and have a nice day :)

MinsangKim-nz
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Amazing video!! Hope there is a statquest on ICA coming soon :)

brucewayne
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Great video; especially liked the echo on the full exposition of 'UMAP' 😂

rajanalexander
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Nice esplanation, i want to use this as my references for my projects

dataanalyticswithmichael
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Thanks so much for the great presentation!

saberkazeminasab
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Great quest, Josh! First time I noticed the fuzzy parts on the circles and arrows. What tool are you using to make the slides? Looks damn fine!

Friedrich