OFER NEIMAN

In many application areas, complex data sets are often represented by some metric space and metric embedding is used to provide a more structured representation of the data. In many of these applications much greater emphasis is put on preserving the local structure of the original space than on maintaining its complete structure. This is also the case in some networking applications where “small world” phenomena in communication patterns have been observed. Practical study of embedding has indeed involved with finding embeddings with this property. In this paper we initiate the study of local embeddings of metric spaces and provide embeddings with distortion depending solely on the local structure of the space.