  We introduce a method called multi-scale local shape analysis, or MLSA, for
extracting features that describe the local structure of points within a
dataset. The method uses both geometric and topological features at multiple
levels of granularity to capture diverse types of local information for
subsequent machine learning algorithms operating on the dataset. Using
synthetic and real dataset examples, we demonstrate significant performance
improvement of classification algorithms constructed for these datasets with
correspondingly augmented features.
