StatQuest: t-SNE, Clearly Explained
Why Cross Validated Stack Exchange recommends it
Cross Validated users link it as a gentle, accurate intuition for how t-SNE computes similarities and places points, and use it as the benchmark for explaining related methods like UMAP.
Recommended by 3 people across 3 different months, 2018 to 2020.
What it is
A roughly 12-minute StatQuest explainer by Josh Starmer on how t-SNE turns a high-dimensional dataset into an easy-to-read low-dimensional graph. It walks through the idea using simple 1D and 2D examples and includes R code for running t-SNE with Rtsne.
What people said
Recommends Bornfree · 2018-02 · Cross Validated Stack Exchange · on “Prediction after PCA and K-Means”
You are probably better off using tSNE [LINK] instead of PCA before running K-Means as: tSNE: Reduces dimensions down using a clustering algorithm ( Video [this video] ) PCA: Reduces dimensions by identifying dominant values
Recommends amoeba · 2019-04 · Cross Validated Stack Exchange · on “Intuitive explanation of how UMAP works, compared to t-SNE”
You said that your understanding of t-SNE is based on [this video] and you are looking for an explanation of UMAP on a similar level. I watched this video and it is pretty accurate in what it says (I have some minor nitpicks, but overall it is fine). Funny enough, it almost applies to UMAP just as it is. Here are things that do not apply: Similarities are computed from distances using a different kernel; it is not Gaussian, but it also decays exponentially and it also has adaptive width, as in t-SNE. Similarities are not normalized to sum to 1, but still end up being normalized to sum a constant value. Similarities are symmetrized, but not just by averaging. The similarity kernel in the embedding
Recommends Dave · 2020-08 · Cross Validated Stack Exchange · on “Why doesn't t-SNE need the labels before visualization?”
In the original Maaten and Hinton paper [LINK] , they explicitly say that the class membership is not used by the t-SNE calculations, only for picking colors in the plot. For all of the data sets, there is information about the class of each datapoint, but the class information is only used to select a color and/or symbol for the map points. The class information is not used to determine the spatial coordinates of the map points. The StatQuest video [this video] on t-SNE, however, makes it seem like the internal workings of the algorithm aims to keep the blue points (red points, orange points) in two dimensions near the blue points (red points, orange points) in one dimension, and I was using tha
Cites Atakan · 2019-04 · Cross Validated Stack Exchange · on “Intuitive explanation of how UMAP works, compared to t-SNE”
I have a PhD in molecular biology. My studies recently started to involve high dimensional data analysis. I got the idea of how t-SNE works (thanks to a StatQuest video on YouTube [this video] ) but can't seem to wrap my mind around UMAP [LINK] (I listened to the UMAP creator's talk [LINK] online but didn't find it easy to understand). I went back to original paper [LINK] describing it but it was too much math for me. Can anybody shed some light on the issue? I am looking or an intuitive explanation, similar to the StatQuest video linked above.
Cites Atakan · 2019-04 · Cross Validated Stack Exchange · on “Intuitive explanation of how UMAP works, compared to t-SNE”
I'm looking for intuition in words but also some simple insight into mathematical calculations (I don't know if the latter is possible). I'd like to see something like this for UMAP: "StatQuest tSNE Clearly Explained" [this video] When I say, I understand how tSNE works, I'm referring to the broad calculation approach described in the video. It is a bit difficult for me to imagine the example in the video in a higher dimensional space, but overall I can see how the distances are calculated. I'd like to have a similar understanding about UMAP