Manifold Learning (often also referred to as non-linear dimensionality reduction) pursuits the goal to embed data that originally lies in a high dimensional space in a lower dimensional space, while preserving characteristic properties. This is possible because for any high dimensional data to be interesting, it must be intrinsically low dimensional. For example, images of faces might be represented as points in a high dimensional space (let’s say your camera has 5MP – so your images, considering each pixel consists of three values , lie in a 15M dimensional space), but not every 5MP image is a face. Faces lie on a sub-manifold in this high dimensional space. A sub-manifold is locally Euclidean, i.e. if you take two very similar points, for example two images of identical twins, you can interpolate between them and still obtain an image on the manifold, but globally not Euclidean – if you take two images that are very different – for example Arnold Schwarzenegger and Hillary Clinton – you cannot interpolate between them. I develop algorithms that map these high dimensional data points into a low dimensional space, while preserving local neighborhoods. This can be interpreted as a non-linear generalization of PCA.