Extracting Topological Features from Big Data Using Persistent Density Entropy
Extracting Topological Features from Big Data Using Persistent Density Entropy is a scholarly work, published in 2019 in ''Journal of Physics: Conference Series''. The main subjects of the publication include microglia, topological data analysis, algebraic number, algebraic topology, persistent homology, big data, topological space, simplicial set, simplicial homology, electronic circuit topology, Entropy, simplicial complex, simplicial approximation theorem, abstract simplicial complex, mathematics, outlier, and topological entropy. The paper defines an entropy called persistent density entropy, which gives the uncertainty of each simplicial complex approximating the underlying space.