Data and ML are the best of friends, yet a lot of issues come with more and bigger data. A large number of attributes or a bloated-up feature space is one common problem. A large feature space poses problems in analyzing and visualizing the data along with issues related to training, memory, and space constraints. This is also known as the curse of dimensionality. Since unsupervised methods help us extract insights and patterns from unlabeled training datasets, they are also useful in helping us reduce dimensionality.

In other words, unsupervised methods help us reduce feature space by helping us select a representative set of features from the complete available list:

Principal Component Analysis (PCA), nearest neighbors, and discriminant analysis are some of the popular dimensionality reduction techniques.

The preceding diagram is a famous depiction of the workings of the PCA based dimensionality reduction technique. It shows a swiss roll shape with data represented in three-dimensional space. Application of PCA results in transformation of the data into two-dimensional space, as shown on the right-hand side of the diagram.