What does perplexity do in t-SNE?
A second feature of t-SNE is a tuneable parameter, “perplexity,” which says (loosely) how to balance attention between local and global aspects of your data. The parameter is, in a sense, a guess about the number of close neighbors each point has. The perplexity value has a complex effect on the resulting pictures.
How many dimensions is the t-SNE?
In this post, I will apply t-SNE to a well-known dataset, called optdigits, for visualisation purposes. The optdigits dataset has 64 dimensions.
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!
Is there a clustering algorithm for t-SNE?
Let’s use 250 points in a normal distribution at (-2,0), and 750 points in a normal distribution at (+2,0). If we run t-SNE with default perplexity of 40, we get an oddly shaped pattern: Not bad, but also not that easy to cluster, is it? You will have a hard time finding a clustering algorithm that works here exactly as desired.
What is equivalent statement can be made about t-SNE clusters?
What equivalent statement can be made about t-SNE clusters? t-SNE reveals approximate contiguity in an underlying high-dimensional manifold, so clusters on the low-dimensional representation of the high-dimensional space maximize the “likelihood” that contiguous individuals will not be in the same cluster
How are points separated in a t-SNE plot?
If points are separated in input space, t-SNE would like to separate them in the low dimensional space. But, it doesn’t care how far they are (unlike PCA, MDS, or isomap, for example). Another issue is that t-SNE sometimes breaks continuous segments of data into pieces and artificially separates them, particularly at low perplexity settings.