Unsupervised Features Learning for Sampled Vector Fields

TL;DR

This paper presents an unsupervised method that converts sampled vector fields into graph structures to extract hidden features, aiding analysis especially when the underlying models are unknown.

Contribution

Introduces a novel approach converting vector fields into graphs for unsupervised feature extraction, applicable to poorly understood data.

Findings

Features correlate with known dynamics in analytic models

Method effective on multiple data sets

Useful for analyzing data without known models

Abstract

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.