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How to report clustering on the output of t-SNE-cross?
What’s more, running a random forest on the data with the cluster assignment as the outcome shows that the clusters have a fairly sensible interpretation given the context of the problem, in terms of the variables that make up the raw data. But if I’m going to report on these clusters, how do I describe them?
How to use t-SNE effectively for visualizing data?
How to Use t-SNE Effectively Although extremely useful for visualizing high-dimensional data, t-SNE plots can sometimes be mysterious or misleading. By exploring how it behaves in simple cases, we can learn to use it more effectively. play_arrowpauserefresh Step linkShare this view Martin WattenbergGoogle Brain Fernanda ViégasGoogle Brain
What is the effect of t-SNE on the shape?
Click here to download the full example code or to run this example in your browser via Binder An illustration of t-SNE on the two concentric circles and the S-curve datasets for different perplexity values. We observe a tendency towards clearer shapes as the perplexity value increases.
How is t-SNE similar to principal component analysis?
PCA and t-SNE. For those who don’t know t-SNE technique (official site), it’s a projection technique -or dimension reduction- similar in some aspects to Principal Component Analysis (PCA), used to visualize N variables into 2 (for example). When the t-SNE output is poor Laurens van der Maaten (t-SNE’s author) says:
Is there a problem with the t-SNE algorithm?
The problem with t-SNE is that it does not preserve distances nor density. It only to some extent preserves nearest-neighbors. The difference is subtle, but affects any density- or distance based algorithm.
What is an equivalent statement about t-SNE-cross?
K-means clusters on principal components reveal individuals who are nearby to one another in terms of the derived variables that comprise X% of the variance in the dataset. What equivalent statement can be made about t-SNE clusters?
Can a t-SNE cluster produce fake patterns?
If we run t-SNE with a too small perplexity such as 20, we get more of these patterns that do not exist: This will cluster e.g. with DBSCAN, but it will yield four clusters. So beware, t-SNE can produce “fake” patterns!