Affinity Learning on Graphs with Diffusion Processes

Abstract

In the thesis, we propose machine learning algorithms utilising diffusion processes to learn the pairwise affinity between data samples. Diffusion processes propagates neighbour information on a node-edge graph, resulting in context-aware affinities that is smooth to the data manifold structure. Similar ideas are also embedded in graph convolutional networks for representation learning. These proposed algorithms improve performance for various machine learning tasks, such as data cluster analysis, dimensionality reduction, and semisupervised classification.