Unsupervised Features Learning for Sampled Vector Fields
This method may be useful for analyzing data sets where the analytic model is poorly understood or not known, but it is incremental as it adapts existing graph tools to vector fields.
The paper tackles the problem of extracting hidden features from sampled vector fields by converting them to a graph structure and applying unsupervised graph analysis tools, showing that the features correlate with known dynamics from analytic models in tested datasets.
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 to 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 generates the data. In particular the method may be useful in analysis of data sets where the analytic model is poorly understood or not known.