t-SNE is a manifold learning technique, which learns low dimensional embeddings for high dimensional data. It is most often used for visualization purposes because it exploits the local relationships between datapoints and can subsequently capture nonlinear structures in the data. Unlike other dimension reduction techniques like PCA, a learned t-SNE model can't be applied to new data. The t-SNE algorithm can be roughly summarized as two steps:

1. Create a probability distribution capturing the relationships between points in the high dimensional space
2. Find a low dimensional space that resembles the probability dimension as well as possible

For further details check out this blog post or the original paper . The implementation of this node is based on T-SNE-Java by Leif Jonsson.

Disclaimer:

Depending on the size of the input table, the computation of t-SNE can be very expensive both in terms of runtime as well as memory. If you experience problems with memory, try to reduce the size of your data by e.g. using the Row Sampling node. If you have very high-dimensional data, it is also advisable to first reduce the number of dimensions to around 50 using e.g. a PCA.