Efficient computation of multipersistent homology and applications to data analysis and visualizationMS16

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) .

Leila De Floriani (University of Maryland)
Federico Iuricich (City University of New York)
multiparameter persistent homology, reduction algorithms, topological data analysis, topology-based data visualization