Physics based supervised and unsupervised learning of graph structure

Graphs are central tools to aid our understanding of biological, physical, and social systems. Graphs also play a key role in representing and understanding the visual world around us, 3D-shapes and 2D-images alike. In this dissertation, I propose the use of physical or natural phenomenon to understand graph structure. I investigate four phenomenon or laws in nature: (1) Brownian motion, (2) Gauss's law, (3) feedback loops, and (3) neural synapses, to discover patterns in graphs.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Mechanical Engineering

Year

2016

Author dc:creator

• Sinha, Ayan

Contributors dc:contributor

• Karthik Ramani
• David Gleich
• Niklas Elmqvist
• Jitesh Panchal

• 3D Computer Vision
• Computational Geometry
• Deep Learning
• Network Science
• Tracking

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/1397

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-2613