TY - JOUR
T1 - ASAP – A sub-sampling approach for preserving topological structures modeled with geodesic topographic mapping
AU - Taghribi, Abolfazl
AU - Canducci, Marco
AU - Mastropietro, Michele
AU - Rijcke, Sven de
AU - Bunte, Kerstin
AU - Tino, Peter
PY - 2022/1/22
Y1 - 2022/1/22
N2 - Topological data analysis tools enjoy increasing popularity in a wide range of applications, such as Computer graphics, Image analysis, Machine learning, and Astronomy for extracting information. However, due to computational complexity, processing large numbers of samples of higher dimensionality quickly becomes infeasible. This contribution is twofold: We present an efficient novel sub-sampling strategy inspired by Coulomb’s law to decrease the number of data points in d-dimensional point clouds while preserving its homology. The method is not only capable of reducing the memory and computation time needed for the construction of different types of simplicial complexes but also preserves the size of the voids in d-dimensions, which is crucial e.g. for astronomical applications. Furthermore, we propose a technique to construct a probabilistic description of the border of significant cycles and cavities inside the point cloud. We demonstrate and empirically compare the strategy in several synthetic scenarios and an astronomical particle simulation of a dwarf galaxy for the detection of superbubbles (supernova signatures).
AB - Topological data analysis tools enjoy increasing popularity in a wide range of applications, such as Computer graphics, Image analysis, Machine learning, and Astronomy for extracting information. However, due to computational complexity, processing large numbers of samples of higher dimensionality quickly becomes infeasible. This contribution is twofold: We present an efficient novel sub-sampling strategy inspired by Coulomb’s law to decrease the number of data points in d-dimensional point clouds while preserving its homology. The method is not only capable of reducing the memory and computation time needed for the construction of different types of simplicial complexes but also preserves the size of the voids in d-dimensions, which is crucial e.g. for astronomical applications. Furthermore, we propose a technique to construct a probabilistic description of the border of significant cycles and cavities inside the point cloud. We demonstrate and empirically compare the strategy in several synthetic scenarios and an astronomical particle simulation of a dwarf galaxy for the detection of superbubbles (supernova signatures).
KW - Generative topographic mapping
KW - Particle simulation
KW - Persistent homology
KW - Probabilistic modeling
KW - Sub-sampling
KW - Supernova shells
KW - Topological data analysis
UR - http://www.scopus.com/inward/record.url?scp=85112671076&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2021.05.108
DO - 10.1016/j.neucom.2021.05.108
M3 - Article
SN - 0925-2312
VL - 470
SP - 376
EP - 388
JO - Neurocomputing
JF - Neurocomputing
ER -