Persistent Laplacian Diagrams
By: Inkee Jung, Wonwoo Kang, Heehyun Park
Potential Business Impact:
Finds hidden shapes in data that other methods miss.
Vectorization methods for \emph{Persistent Homology} (PH), such as the \emph{Persistence Image} (PI), encode persistence diagrams into finite dimensional vector spaces while preserving stability. In parallel, the \emph{Persistent Laplacian} (PL) has been proposed, whose spectra contain the information of PH as well as richer geometric and combinatorial features. In this work, we develop an analogous vectorization for PL. We introduce \emph{signatures} that map PL to real values and assemble these into a \emph{Persistent Laplacian Diagram} (PLD) and a \emph{Persistent Laplacian Image} (PLI). We prove the stability of PLI under the noise on PD. Furthermore, we illustrate the resulting framework on explicit graph examples that are indistinguishable by both PH and a signature of the combinatorial Laplacian but are separated by the signature of PL.
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