
Extracting Topological Features from Big Data Using Persistent Density Entropy
Author(s) -
Jinzhong Xu,
Xuzhi Li,
Hongfei Wang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1168/3/032017
Subject(s) - persistent homology , topological data analysis , simplicial complex , outlier , abstract simplicial complex , mathematics , simplicial homology , entropy (arrow of time) , simplicial approximation theorem , topological entropy , algebraic topology , algebraic number , topological space , topology (electrical circuits) , simplicial set , pure mathematics , algorithm , combinatorics , physics , quantum mechanics , mathematical analysis , statistics , homotopy , homotopy category