Manifold Learning belongs to the categories of Artificial Intelligence and Big Data and Smart Data. It describes a specific method in the field of data analysis. The goal of Manifold Learning is to represent very large and complex datasets in a way that makes them more understandable to humans.<\/p>\n
Imagine you have a huge table with thousands of measurements on your customers' purchasing behaviour. The data is difficult to interpret and contains many different influencing factors. Using Manifold Learning, a computer can simplify these many data points into a simpler form, for example, an easy-to-understand two-dimensional graphic. This allows patterns or groups in the data to be recognised at a glance.<\/p>\n
A vivid example: In industry, Manifold Learning is used to detect hidden faults in complex machine data early on. By \u201eunravelling\u201c the data with Manifold Learning, experts can recognise when and where deviations occur that could indicate a problem.<\/p>\n
So, manifold learning helps to find hidden connections in large datasets. This enables better decisions and can make processes significantly more efficient.<\/p>","protected":false},"excerpt":{"rendered":"