Unsupervised Features Learning for Sampled Vector Fields

Explore a novel approach to computing hidden features of sampled vector fields in this hour-long conference talk. Learn how to convert vector field data into graph structures and utilize tools designed for automatic, unsupervised graph analysis. Examine case studies demonstrating the correlation between collected vector field features and known dynamics of analytic models generating the data. Discover the potential applications of this method in analyzing datasets where analytic models are poorly understood or unknown, offering valuable insights for researchers and data scientists working with complex vector field data.

Mateus Juda (7/29/20): Unsupervised features learning for sampled vector fields