A1283
Title: Topological clustering and inference on heat-diffusion estimates of persistence diagrams
Authors: Yuan Wang - University of South Carolina (United States) [presenting]
Jian Yin - Nanjing Medical University (China)
Abstract: Topological data analysis (TDA) has motivated the exploration of mesoscale features in brain signals and networks. Persistent homology is a key TDA algorithm for decoding and representing these features via persistence descriptors such as persistence diagram (PD), whose statistical significance is often revealed through permutation testing. But testing on PDs is challenging due to the heterogeneity of points in the diagrams that encode the birth and death times of features through a dynamic filtration of the subnetworks. The purpose is to showcase a topological clustering and transposition-based permutation testing framework based on heat-diffusion estimates of PDs to resolve computational bottlenecks of heavy permutations, with applications to the comparison of brain networks constructed from neuroimaging data.
