# A computationally efficient framework for vector representation of persistence diagrams

@article{Chan2021ACE, title={A computationally efficient framework for vector representation of persistence diagrams}, author={Kit C. Chan and Umar Islambekov and A. V. Luchinsky and Rebecca Sanders}, journal={ArXiv}, year={2021}, volume={abs/2109.08239} }

In Topological Data Analysis, a common way of quantifying the shape of data is to use a persistence diagram (PD). PDs are multisets of points in R computed using tools of algebraic topology. However, this multi-set structure limits the utility of PDs in applications. Therefore, in recent years efforts have been directed towards extracting informative and efficient summaries from PDs to broaden the scope of their use for machine learning tasks. We propose a computationally efficient framework to…

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