Nonlinear methods of dimension reduction and their applications

When processing large volumes of data coming from automated measurements, it is necessary to go beyond the commonly used linear methods, of which the principal component analysis is a typical representative. The aim of this bachelor thesis will be to investigate selected nonlinear dimension reduction methods, of which manifold learning and autoencoders are typical representatives, and apply them to own data.