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arXiv · 1811.05396

Computing multiparameter persistent homology through a discrete Morse-based approach

Sara Scaramuccia·Federico Iuricich·Leila De Floriani·Claudia Landi
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Abstract

Persistent Homology (PH) allows tracking homology features like loops, holes and their higher-dimensional analogs, along with a single-parameter family of nested spaces. Currently, computing descriptors for complex data characterized by multiple functions is becoming an important task in several applications, including physics, chemistry, medicine, geography, etc. Multiparameter Persistent Homology (MPH) generalizes persistent homology opening to the exploration and analysis of shapes endowed with multiple filtering functions. Still, computational constraints prevent MPH to be feasible over real-sized data. In this paper, we consider discrete Morse Theory as a tool to simplify the computation of MPH on a multiparameter dataset. We propose a new algorithm, well suited for parallel and distributed implementations and we provide the first evaluation of the impact on MPH computations of a preprocessing approach.

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Sara Scaramuccia, Federico Iuricich, Leila De Floriani, Claudia Landi. 2018-11-13. Computing multiparameter persistent homology through a discrete Morse-based approach. https://doi.org/10.1016/j.comgeo.2020.101623

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