In the previous chapter, we apply t-SNE to visualize the newsgroup text data in reduced 2 dimensions. T-SNE, or dimensionality reduction in general, is a type of unsupervised learning. Instead of having a teacher educating what particular output to produce, be it a class or membership (classification), be it a continuous value (regression), unsupervised learning identifies inherent structures or commonalities in the input data. Since there is no guidance in unsupervised learning, there is no clear answer on what is a right or wrong result. Unsupervised learning has the freedom to discover hidden information underneath input data.
An easy way to understand unsupervised learning is to think ...
