Real data of natural and social sciences is often very high-dimensional. However, the underlying structure can in many cases be described by a small number of features. Recently two new non-linear methods for reducing the dimensionality, Locally Linear Embedding and Isomap, have been suggested and successfully applied. This talk presents both algorithms and compares them by means of several synthetic and real data sets.