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\newtheorem{prop}{Proposition} \newtheorem{cor}{Corollary} \newtheorem*{utheorem}{Theorem} \newtheorem*{ulemma}{Lemma} \newtheorem*{uprop}{Proposition} \newtheorem*{ucor}{Corollary} \theoremstyle{definition} \newtheorem{defn}{Definition} \newtheorem{example}{Example} \newtheorem*{udefn}{Definition} \newtheorem*{uexample}{Example} \theoremstyle{remark} \newtheorem{remark}{Remark} \newtheorem{note}{Note} \newtheorem*{uremark}{Remark} \newtheorem*{unote}{Note} %------------------------------------------------------------------- \begin{document} %------------------------------------------------------------------- \section*{persistent homology} \hypertarget{context}{}\subsubsection*{{Context}}\label{context} \hypertarget{cohomology}{}\paragraph*{{Cohomology}}\label{cohomology} [[!include cohomology - contents]] \hypertarget{constructivism_realizability_computability}{}\paragraph*{{Constructivism, Realizability, Computability}}\label{constructivism_realizability_computability} [[!include constructivism - contents]] \hypertarget{persistent_homology}{}\section*{{Persistent homology}}\label{persistent_homology} \noindent\hyperlink{idea}{Idea}\dotfill \pageref*{idea} \linebreak \noindent\hyperlink{more_detail}{More detail}\dotfill \pageref*{more_detail} \linebreak \noindent\hyperlink{talks}{Talks}\dotfill \pageref*{talks} \linebreak \noindent\hyperlink{software}{Software}\dotfill \pageref*{software} \linebreak \noindent\hyperlink{related_concepts}{Related concepts}\dotfill \pageref*{related_concepts} \linebreak \noindent\hyperlink{references}{References}\dotfill \pageref*{references} \linebreak \noindent\hyperlink{general}{General}\dotfill \pageref*{general} \linebreak \noindent\hyperlink{variants}{Variants}\dotfill \pageref*{variants} \linebreak \hypertarget{idea}{}\subsection*{{Idea}}\label{idea} Persistent homology is a [[homology theory]] adapted to a [[computation|computational]] context, for instance, in analysis of large data sets. It keeps track of homology classes which stay `persistent' when the approximate image of a space gets refined to higher resolutions. \hypertarget{more_detail}{}\subsection*{{More detail}}\label{more_detail} We suppose given a `data cloud of samples', $P\subset \mathbb{R}^m$, from some space $X$, yielding a [[simplicial complex]] $S_\rho(X)$ for each $\rho \gt 0$ via one of the family of simplicial complex approximation methods that are listed below (TO BE ADDED). For these, the important idea to retain is that if $\rho \lt \rho^\prime$, then \begin{displaymath} S_\rho(X) \hookrightarrow S_{\rho^\prime}(X), \end{displaymath} so we get a `filtration structure' on the complex. The idea of \emph{persistent homology} is to look for features that persist for some range of parameter values. Typically a feature, such as a hole, will initially not be observed, then will appear, and after a range of values of the parameter it will disappear again. A typical feature will be a [[Betti number]] of the complex, $S_\rho(X)$, which then will vary with the parameter $\rho$. \hypertarget{talks}{}\subsection*{{Talks}}\label{talks} \begin{itemize}% \item [[ACT-OIT.pdf|Slides:file]] for a talk by [[Tim Porter]] in 2008. \end{itemize} \hypertarget{software}{}\subsection*{{Software}}\label{software} One can compute intervals for homological features algorithmically over field coefficients and software packages are available for this purpose. See for instance \href{http://www.sas.upenn.edu/~vnanda/perseus}{Perseus}. The principal algorithm is based on bringing the filtered complex to its \textbf{canonical form} by upper-triangular matrices from (\href{https://www.researchgate.net/profile/Serguei_Barannikov/publication/267672645_The_Framed_Morse_complex_and_its_invariants/links/5970e947458515fa8de6e724/The-Framed-Morse-complex-and-its-invariants.pdf}{Barannikov1994, §2.1}) \hypertarget{related_concepts}{}\subsection*{{Related concepts}}\label{related_concepts} \begin{itemize}% \item [[persistence module]] \end{itemize} \hypertarget{references}{}\subsection*{{References}}\label{references} \hypertarget{general}{}\subsubsection*{{General}}\label{general} A clear introduction to the use of persistent homology in data analysis is \begin{itemize}% \item [[Robert Ghrist]], \emph{Barcodes: The Persistent Topology of Data}, (\href{https://www.math.upenn.edu/~ghrist/preprints/barcodes.pdf}{pdf}) \end{itemize} Bar-codes were discovered under the name of \emph{canonical forms invariants of filtered complexes} in \begin{itemize}% \item Serguei Barannikov, \emph{Framed Morse complex and its invariants}, \href{https://www.researchgate.net/profile/Serguei_Barannikov/publication/267672645_The_Framed_Morse_complex_and_its_invariants/links/5970e947458515fa8de6e724/The-Framed-Morse-complex-and-its-invariants.pdf}{pdf} Advances in Soviet Mathematics \textbf{21} 93–115 (1994) \end{itemize} Other references: \begin{itemize}% \item D. Le Peutrec, N. Nier, C. Viterbo, \emph{Precise Arrhenius Law for p-forms: The Witten Laplacian and Morse–Barannikov Complex}, Annales Henri Poincaré \textbf{14} (3): 567–610 (2013) \href{https://10.1007/s00023-012-0193-9}{doi} \item [[Robert MacPherson]], Benjamin Schweinhart, \emph{Measuring shape with topology}, J. Math. Phys. \textbf{53}, 073516 (2012); \href{http://dx.doi.org/10.1063/1.4737391}{doi} \item A. Zomorodian, [[Gunnar Carlsson]], \emph{Computing persistent homology}, Discrete Comput. Geom. \textbf{33}, 249--274 (2005) \item Ulrich Bauer, Michael Kerber, Jan Reininghaus, \emph{Clear and compress: computing persistent homology in chunks}, \href{http://arxiv.org/abs/1303.0477}{arxiv/1303.0477} \item Robert J. Adler, Omer Bobrowski, Matthew S. Borman, Eliran Subag, Shmuel Weinberger, \emph{Persistent homology for random fields and complexes} Institute of Mathematical Statistics Collections \textbf{6}:124--143, 2010 \href{http://arxiv.org/abs/1003.1001}{arxiv/1003.1001} \item Robert J. Adler, Omer Bobrowski, Shmuel Weinberger, \emph{Crackle: the persistent homology of noise}, \href{http://arxiv.org/abs/1301.1466}{arxiv/1301.1466} \item Pawe Dotko, Hubert Wagner, \emph{Computing homology and persistent homology using iterated Morse decomposition}, \href{http://arxiv.org/abs/1210.1429}{arxiv/1210.1429} \item [[Gunnar Carlsson]], V. de Silva, \emph{Zigzag persistence}, \href{http://arxiv.org/abs/0812.0197}{arXiv:0812.0197} \item [[Gunnar Carlsson]], \emph{Topology and data}, Bull. Amer. Math. Soc. 46 (2009), no. 2, 255-308. \item Francisco Belch\'i{} Guillam\'o{}n, Aniceto Murillo Mas, \emph{A-infinity persistence}, \href{http://arxiv.org/abs/1403.2395}{arxiv/1403.2395} \item Jo\~a{}o Pita Costa, Mikael Vejdemo Johansson, Primo \v{S}kraba, \emph{Variable sets over an algebra of lifetimes: a contribution of lattice theory to the study of computational topology}, \href{http://arxiv.org/abs/1409.8613}{arxiv/1409.8613} \item Genki Kusano, Kenji Fukumizu, Yasuaki Hiraoka, \emph{Persistence weighted Gaussian kernel for topological data analysis}, \href{http://arxiv.org/abs/1601.01741}{arxiv/1601.01741} \item Heather A. Harrington, Nina Otter, Hal Schenck, [[Ulrike Tillmann]], \emph{Stratifying multiparameter persistent homology}, \href{https://arxiv.org/abs/1708.07390}{arxiv/1708.07390} \item H. Edelsbrunner, D. Morozov, \emph{Persistent homology: theory and practice} \href{http://mrzv.org/publications/persistent-homology-theory-practice/ecm}{pdf} \end{itemize} The following paper uses persistent homology to single out features relevant for training neural networks, \begin{itemize}% \item Jean-Baptiste Bardin, Gard Spreemann, [[Kathryn Hess]], \emph{Topological exploration of artificial neuronal network dynamics}, \href{https://arxiv.org/abs/1810.01747}{arxiv/1810.01747} \end{itemize} \hypertarget{variants}{}\subsubsection*{{Variants}}\label{variants} Discussion of persistent [[Cohomotopy]]: \begin{itemize}% \item [[Peter Franek]], [[Marek Krčál]], \emph{Persistence of Zero Sets}, Homology, Homotopy and Applications, Volume 19 (2017) Number 2 (\href{https://arxiv.org/abs/1507.04310}{arXiv:1507.04310}, \href{http://dx.doi.org/10.4310/HHA.2017.v19.n2.a16}{doi:10.4310/HHA.2017.v19.n2.a16}) \item [[Peter Franek]], [[Marek Krčál]], \emph{Cohomotopy groups capture robust Properties of Zero Sets via Homotopy Theory}, talk at \href{https://www2.ist.ac.at/acat}{ACAT meeting 2015} (\href{https://www2.ist.ac.at/fileadmin/user_upload/events_pages/acat/ACAT2015_Marek_Krcal.pdf}{pfd slides}) \item [[Peter Franek]], [[Marek Krčál]], Hubert Wagner, \emph{Solving equations and optimization problems with uncertainty}, J Appl. and Comput. Topology (2018) 1: 297 (\href{https://arxiv.org/abs/1607.06344}{arxiv:1607.06344}, \href{https://doi.org/10.1007/s41468-017-0009-6}{doi:10.1007/s41468-017-0009-6}) \end{itemize} category: topology, applications [[!redirects persistent homology]] [[!redirects persistent homology theory]] [[!redirects persistence module]] [[!redirects persistence modules]] \end{document}