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Computing persistent features in big data: A distributed dimension reduction approach | IEEE Conference Publication | IEEE Xplore

Computing persistent features in big data: A distributed dimension reduction approach


Abstract:

Persistent homology has become one of the most popular tools used in topological data analysis for analyzing big data sets. In an effort to minimize the computational com...Show More

Abstract:

Persistent homology has become one of the most popular tools used in topological data analysis for analyzing big data sets. In an effort to minimize the computational complexity of finding the persistent homology of a data set, we develop a simplicial collapse algorithm called the selective collapse. This algorithm works by representing the previously developed strong collapse as a forest and uses that forest data to improve the speed of both the strong collapse and of persistent homology. Finally, we demonstrate the savings in computational complexity using geometric random graphs.
Date of Conference: 04-09 May 2014
Date Added to IEEE Xplore: 14 July 2014
Electronic ISBN:978-1-4799-2893-4

ISSN Information:

Conference Location: Florence, Italy

References

References is not available for this document.