Topological data analysis (TDA) detects geometric structure in biological data. However, many TDA algorithms are memory intensive and impractical for massive datasets. Here, we introduce a statistical protocol that reduces TDA's memory requirements and gives access to scientists with modest computing resources. We validate this protocol against two empirical datasets, showing that it replicates previous findings with much lower memory requirements. Finally, we demonstrate the power of the protocol by mapping the topology of functional correlations for the human cortex at high spatial resolution, something that was previously infeasible without this novel approach.
@article{arxiv.2509.03681,
title = {ALBATROSS: Cheap Filtration Based Geometry via Stochastic Sub-Sampling},
author = {Andrew J. Stier and Naichen Shi and Raed Al Kontar and Chad Giusti and Marc G. Berman},
journal= {arXiv preprint arXiv:2509.03681},
year = {2025}
}