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:

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