Unsupervised learning might come in many shapes and forms, but the goal is always to convert original data into a richer, more meaningful representation, whether that means making it easier for humans to understand or easier for machine learning algorithms to parse.
Some common applications of unsupervised learning include the following:
- Dimensionality reduction: This takes a high-dimensional representation of data consisting of many features and tries to compress the data so that its main characteristics can be explained with a small number of highly informative features. For example, when applied to housing prices in the neighborhoods of Boston, dimensionality reduction might be able to tell us that the indicators we should pay most attention to are the property tax and the neighborhood's crime rate.
- Factor analysis: This tries to find...
