From the course: Data Science Foundations: Data Mining in R
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t-SNE
From the course: Data Science Foundations: Data Mining in R
t-SNE
- [Instructor] The final method of dimensionality reduction I want to demonstrate in R is t-SNE, which is short for, t-distributed stochastic neighbor embedding. This is a relatively new, very sophisticated approach and what it has as its great virtue is the ability to do nonlinear separation. When you can't draw straight lines between groups or you have one group that's completely surrounded by another, or you have overlapping curves, t-SNE is usually able to separate them because of the algorithm that it uses. Now t-SNE is generally used for visualization, and so let me show you how this works. We're going to load a few packages. The important one here is rtsne. So we're going to load those. And then I'm going to use the complete dataset, again, it's because t-SNE is primarily an exploratory approach, so we're not going to do the training and testing, but we'll just use all of the data at once. So I'll import the…
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