We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ implementations.
We convert a PD to a finite-dimensional vector representation which we call a persistence image (PI), and prove the stability of this transformation with respect to small perturbations in the inputs.
#4 best model for Graph Classification on NEURON-Average
Persistence diagrams, the most common descriptors of Topological Data Analysis, encode topological properties of data and have already proved pivotal in many different applications of data science.
We propose to apply the mapper construction--a popular tool in topological data analysis--to graph visualization, which provides a strong theoretical basis for summarizing network data while preserving their core structures.
While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data.