Learning simplicial complexes from persistence diagrams
arXiv preprint arXiv:1805.10716, 2018•arxiv.org
Topological Data Analysis (TDA) studies the shape of data. A common topological descriptor
is the persistence diagram, which encodes topological features in a topological space at
different scales. Turner, Mukeherjee, and Boyer showed that one can reconstruct a simplicial
complex embedded in R^ 3 using persistence diagrams generated from all possible height
filtrations (an uncountably infinite number of directions). In this paper, we present an
algorithm for reconstructing plane graphs K=(V, E) in R^ 2, ie, a planar graph with vertices in …
is the persistence diagram, which encodes topological features in a topological space at
different scales. Turner, Mukeherjee, and Boyer showed that one can reconstruct a simplicial
complex embedded in R^ 3 using persistence diagrams generated from all possible height
filtrations (an uncountably infinite number of directions). In this paper, we present an
algorithm for reconstructing plane graphs K=(V, E) in R^ 2, ie, a planar graph with vertices in …
Topological Data Analysis (TDA) studies the shape of data. A common topological descriptor is the persistence diagram, which encodes topological features in a topological space at different scales. Turner, Mukeherjee, and Boyer showed that one can reconstruct a simplicial complex embedded in R^3 using persistence diagrams generated from all possible height filtrations (an uncountably infinite number of directions). In this paper, we present an algorithm for reconstructing plane graphs K=(V,E) in R^2 , i.e., a planar graph with vertices in general position and a straight-line embedding, from a quadratic number height filtrations and their respective persistence diagrams.
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