Optimal Transport Based Ordinal Pattern Tree Kernel for Brain Disease Diagnosis

Abstract

AbstractFinding a faithful connection pattern of brain network is a challenging task for most of the existing methods in brain network analysis. To process this problem, we propose a novel method called ordinal pattern tree (OPT) for representing the connection pattern of network by using the ordinal pattern relationships of edge weights in brain network. On OPT, nodes are connected by ordinal edges which make nodes have hierarchical structures. The changes of edge weights in brain network will affect ordinal edges and result in the differences of OPTs. We further leverage optimal transport distances to measure the transport costs between the nodes on the paired of OPTs. Based on these optimal transport distances, we develop a new graph kernel called optimal transport based ordinal pattern tree kernel to measure the similarity between the paired brain networks. To evaluate the effectiveness of the proposed method, we perform classification and regression experiments in functional magnetic resonance imaging data of brain diseases. The experimental results demonstrate that our proposed method can achieve significant improvement compared with the state-of-the-art graph kernel methods on classification and regression tasks.KeywordsGraph kernelOrdinal pattern treeBrain networkClassificationOptimal transport