StatQuest: t-SNE, Clearly Explained

t-SNE is a popular method for making an easy to read graph from a complex dataset, but not many people know how it works. Here's the inside scoop.

Here’s how to create a t-SNE graph in R (this is copied from the help file for Rtsne)…

library("Rtsne")
iris_unique <- unique(iris) # Remove duplicates
tsne_out <- Rtsne(iris_matrix) # Run TSNE

# Show the objects in the 2D tsne representation
plot(tsne_out$Y,col=iris_unique$Species)

For a complete index of all the StatQuest videos, check out:

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

Buying The StatQuest Illustrated Guide to Machine Learning!!!

...or...

...a cool StatQuest t-shirt or sweatshirt:

...buying one or two of my songs (or go large and get a whole album!)

...or just donating to StatQuest!

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

0:00 Awesome song and introduction
1:19 Overview of what t-SNE does
2:24 Overview of how t-SNE works
4:12 Step 1: Determine high-dimensional similarities
9:26 Step 2: Determine low-dimensional similarities
10:33 Step 3: Move points in low-d
11:05 Why the t-distribution is used instead of the normal distribution

Corrections:
6:17 I should have said that the blue points have twice the density of the purple points.
7:08 There should be a 0.05 in the denominator, not a 0.5.

#statquest #tsne

[statquest]

Corrections:
6:17 I should have said that the blue points have twice the density of the purple points.
7:08 There should be a 0.05 in the denominator, not a 0.5.

[tuongminhquoc]

Thank you. I am not sure if you remember me from the PCA video. I have a job now. My job do not have high salary, but I could now support you by donating and thank you now. 😊

[jumpinggene]

Your videos have helped me a lot over many years now. Your slow, step-wise delivery is good for students at any age. Thank you so much 👏

[abdulgadirhussein]

I am always blown away by how you make statistics & machine learning algorithms so simple to understand and how you graciously share your knowldege. Keep up the great work man, you are awesome!

[잠꾸러기-gs]

Whenever I find statistics technique I have never seen in scientific article, I always visit your channel. Thanks a lot!!

[veronikaberezhnaia]

I regret I can't put 1000 likes! I read about 20 articles about t-SNE, they are similar to one another, almost identical - and they don't get me closer to the point. But your video - I watched it 4 times (because the topic is hard, at least for me) with making some and drawing - but finally I understand how it works, up to the point that I can explain it to someone else. So many thanks to you!

[RezaRob]

I'm writing this comment while having watched only half way into this video, which is pretty unusual for me!

It is so clearly explained! I once glanced at the t-SNE paper and didn't understand it. If this is what it does then this is how things like this should be explained!

Really, we need people explaining science like this! It's possible to read scientific papers, but what they fail to do is properly communicate the core idea to the reader so that the reader quickly grasps the big picture and the intent of the mathematical details without getting lost in the details.

Frequently, even a missing definition can make reading papers much harder for non experts.

[douglasaraujo]

As entertaining as watching a Walt t-SNE movie!

[gustavomorais]

I never leave comments, but I really feel the need to thank you for being able to explain this in such a simple way

[jjlian]

Josh is so far my favorite YouTuber that is able to explain complex stats concepts so smoothly.

[OnSightNoMore]

It's impressive how you managed to explain the essential concepts of this chain of algorithms in such a clear way! I'm sharing this video with my beginner fellows, who normally flee as soon as I say words like nearest-neighbor or stochastic.
Thank you very much!

[gayathrikurada]

Josh.. Your explanation is always "simple and easy to understand" even for layman.You are simply "The life Saviour" !!!
Thank you so much :)

[edridgedsouza]

"This is Josh Starmer, and you're watching Tisney Channel!"

[sarangak.mahanta]

The only educational channel which brings a smile to my face.

[thedrunkprogrammer]

I really can't appreciate you enough for your videos.
Books and blogs only make sense after I watch your videos!

[kass]

I never knew machine learning could be as simple as... BAM

[atakanekiz]

Great explanations! Can you please do a video explaining UMAP and potentially how it compares to t-SNE? Thanks!

[DoanQuocHoan]

I was so confusing about t-SNE until I watched this. It's clear and very easy to understand! Thank you! Like your BAM. :D

[nanopore-sequence]

I am a student in Japan.
I'm not good at English, but it was very easy to understand and I learned a lot:)

[snackbob]

Josh, i literally love your videos, they are really helping me get through my ADV CS degree. I am going to buy one of your shirts, and wear it on campus as a thank you!