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Parallel & scalable zig-zag persistent homology
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0001-6322-7542
Jozef Stefan Institute. (AI Laboratory)
2012 (English)Conference paper, Poster (Refereed)
Abstract [en]

By computing repeated pullbacks, we are able to compute zig-zag persistent homology in a way that easily parallelizes. In this paper, we demonstrate this algorithm together with its underlying mathematical foundation. We can parallelizeand scale the computation scheme.

Place, publisher, year, edition, pages
National Category
Computational Mathematics
URN: urn:nbn:se:kth:diva-107663OAI: diva2:576994
NIPS 2012 Workshop on Algebraic Topology and Machine Learning,December 7-8, 2012,Lake Tahoe, Nevada, United States
EU, FP7, Seventh Framework Programme, FP7-ICT-318493-STREP

QC 20130116

Available from: 2013-01-16 Created: 2012-12-14 Last updated: 2013-01-16Bibliographically approved

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fulltext(53 kB)250 downloads
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