Unsupervised Features Learning for Sampled Vector Fields
Mateusz Juda · SIAM Journal on Applied Dynamical Systems · 2020
In this paper we introduce a new approach to computing hidden features of sampled vector fields. The basic idea is to convert the vector field data into a graph structure and use tools designed for automatic, unsupervised analysis of graphs. Using a few data sets, we show that the collected features of the vector fields are correlated with the dynamics known for analytic models which generate the data. In particular the method may be useful in the analysis of data sets where the analytic model is poorly understood or not known.