4 research outputs found
Sparse Nerves in Practice
Topological data analysis combines machine learning with methods from
algebraic topology. Persistent homology, a method to characterize topological
features occurring in data at multiple scales is of particular interest. A
major obstacle to the wide-spread use of persistent homology is its
computational complexity. In order to be able to calculate persistent homology
of large datasets, a number of approximations can be applied in order to reduce
its complexity. We propose algorithms for calculation of approximate sparse
nerves for classes of Dowker dissimilarities including all finite Dowker
dissimilarities and Dowker dissimilarities whose homology is Cech persistent
homology. All other sparsification methods and software packages that we are
aware of calculate persistent homology with either an additive or a
multiplicative interleaving. In dowker_homology, we allow for any
non-decreasing interleaving function . We analyze the computational
complexity of the algorithms and present some benchmarks. For Euclidean data in
dimensions larger than three, the sizes of simplicial complexes we create are
in general smaller than the ones created by SimBa. Especially when calculating
persistent homology in higher homology dimensions, the differences can become
substantial
Sparse Nerves in Practice
Topological data analysis combines machine learning with methods from algebraic topology. Persistent homology, a method to characterize topological features occurring in data at multiple scales is of particular interest. A major obstacle to the wide-spread use of persistent homology is its computational complexity. In order to be able to calculate persistent homology of large datasets, a number of approximations can be applied in order to reduce its complexity. We propose algorithms for calculation of approximate sparse nerves for classes of Dowker dissimilarities including all finite Dowker dissimilarities and Dowker dissimilarities whose homology is Čech persistent homology. All other sparsification methods and software packages that we are aware of calculate persistent homology with either an additive or a multiplicative interleaving. In dowker_homology, we allow for any non-decreasing interleaving function α . We analyze the computational complexity of the algorithms and present some benchmarks. For Euclidean data in dimensions larger than three, the sizes of simplicial complexes we create are in general smaller than the ones created by SimBa. Especially when calculating persistent homology in higher homology dimensions, the differences can become substantial.acceptedVersio
Using persistent homology to reveal hidden covariates in systems governed by the kinetic Ising model
We propose a method, based on persistent homology, to uncover topological properties of a priori unknown covariates in a system governed by the kinetic Ising model with time-varying external fields. As its starting point the method takes observations of the system under study, a list of suspected or known covariates, and observations of those covariates. We infer away the contributions of the suspected or known covariates, after which persistent homology reveals topological information about unknown remaining covariates. Our motivating example system is the activity of neurons tuned to the covariates physical position and head direction, but the method is far more general