Back to Glossary

Unsupervised Learning

Given several objects/samples x1, … , xn, the goal is to learn a hidden map h(x) that can uncover a hidden structure in the data. This hidden map can be used to ‘compress’ x (aka dimensionality reduction) or to assign to every xi a group ck (aka clustering or topic modelling). In other words, unsupervised learning does not use labelled data, and the model instead recognises patterns in the data to compute an output.

Last Updated:

Discover how we can help your company

Schedule a call with one of our experts

Schedule a call