What is a t-SNE plot used for?

What is a t-SNE plot used for?

t-Distributed Stochastic Neighbor Embedding (t-SNE) is an unsupervised, non-linear technique primarily used for data exploration and visualizing high-dimensional data. In simpler terms, t-SNE gives you a feel or intuition of how the data is arranged in a high-dimensional space.

What does T-SNE stand for?

t-distributed stochastic neighbor embedding
t-distributed stochastic neighbor embedding (t-SNE) is a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map.

Is SNE supervised?

Wikipedia classifies the t-sne algorithm as a supervised method.

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.

How are t-SNE plots used in data visualization?

One way that t-SNE visualizations can be useful is by combining them with external information. This can reveal patterns in the data that we may not have been aware of. For example, the t-SNE papers show visualizations of the MNIST dataset (images of handwritten digits).

Can you see relative size of clusters in t-SNE plot?

Rather, density equalization happens by design and is a predictable feature of t-SNE. The bottom line, however, is that you cannot see relative sizes of clusters in a t-SNE plot. 3. Distances between clusters might not mean anything

Do you know how to use t-SNE effectively?

We’ll walk through a series of simple examples to illustrate what t-SNE diagrams can and cannot show. The t-SNE technique really is useful—but only if you know how to interpret it. Before diving in: if you haven’t encountered t-SNE before, here’s what you need to know about the math behind it.