In this talk, we will focus on computational tools for the analysis of large size datasets equipped with multiple scalar values. We will consider multi-persistent homology, an extension of persistent homology motivated by the need to analyze properties of data characterized by several parameters. We will present a new approach to multi-persistent homology computation based on reducing the input complex through the definition of a discrete gradient field inspired by discrete Morse theory.

This presentation is part of Minisymposium “MS16 - Topological Image Analysis: Methods, Algorithms, Applications (3 parts)”
organized by: Patrizio Frosini (University of Bologna) , Massimo Ferri (University of Bologna) , Claudia Landi (University of Modena and Reggio Emilia) .

Authors:

Leila De Floriani (University of Maryland)

Federico Iuricich (City University of New York)

Keywords:

multiparameter persistent homology, reduction algorithms, topological data analysis, topology-based data visualization