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Structure theorem

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  Structure theorem [ edit ] The structure theorem is of central importance to TDA; as commented by G. Carlsson, "what makes homology useful as a discriminator between topological spaces is the fact that there is a classification theorem for finitely generated abelian groups." [3]  (see the  fundamental theorem of finitely generated abelian groups ). The main argument used in the proof of the original structure theorem is the standard  structure theorem for finitely generated modules over a principal ideal domain . [9]  However, this argument fails if the indexing set is  ( � , ≤ ) . [3] In general, not every persistence module can be decomposed into intervals. [70]  Many attempts have been made at relaxing the restrictions of the original structure theorem. [ clarification needed ]  The case for pointwise finite-dimensional persistence modules indexed by a locally finite subset of  �  is solved based on the work of Webb. [71]  The most notable result is done by Crawley-Boeve

Topological data analysis

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  Topological data analysis 5 languages Article Talk Read Edit View history From Wikipedia, the free encyclopedia In  applied mathematics ,  topological based data analysis  ( TDA ) is an approach to the analysis of datasets using techniques from  topology . Extraction of information from datasets that are high-dimensional, incomplete and noisy is generally challenging. TDA provides a general framework to analyze such data in a manner that is insensitive to the particular  metric  chosen and provides  dimensionality reduction  and robustness to noise. Beyond this, it inherits  functoriality , a fundamental concept of modern mathematics, from its topological nature, which allows it to adapt to new mathematical tools. [ citation needed ] The initial motivation is to study the shape of data. TDA has combined  algebraic topology  and other tools from pure mathematics to allow mathematically rigorous study of "shape". The main tool is  persistent homology , an adaptation of  homol