Machine Learning for OpenCV 4 - Second Edition

Understanding unsupervised learning

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 ...