6. t-Distributed Stochastic Neighbor Embedding

Overview

In this chapter, we will discuss Stochastic Neighbor Embedding (SNE) and t-Distributed Stochastic Neighbor Embedding (t-SNE) as a means of visualizing high-dimensional datasets. We will implement t-SNE models in scikit-learn and explain the limitations of t-SNE. Being able to extract high-dimensional information into lower dimensions will prove helpful for visualization and exploratory analysis, as well as being helpful in conjunction with the clustering algorithms we explored in prior chapters. By the end of this chapter, we will be able to find clusters in high-dimensional data, such as user-level information or images in a low-dimensional space.