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The Compressed Annotation Matrix: an Efficient Data Structure for Computing Persistent Cohomology

Jean-Daniel Boissonnat 1 Tamal K. Dey 2 Clément Maria 1, *
* Corresponding author
1 DATASHAPE - Understanding the Shape of Data
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Saclay - Ile de France
Abstract : The persistent homology with coefficients in a field F coincides with the same for cohomology because of duality. We propose an implementation of a recently introduced algorithm for persistent cohomology that attaches annotation vectors with the simplices. We separate the representation of the simplicial complex from the representation of the cohomology groups, and introduce a new data structure for maintaining the annotation matrix, which is more compact and reduces substancially the amount of matrix operations. In addition, we propose heuristics to simplify further the representation of the cohomology groups and improve both time and space complexities. The paper provides a theoretical analysis, as well as a detailed experimental study of our implementation and comparison with state-of-the-art software for persistent homology and cohomology.
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https://hal.inria.fr/hal-00761468
Contributor : Clément Maria <>
Submitted on : Monday, January 6, 2020 - 7:02:29 PM
Last modification on : Friday, January 10, 2020 - 1:24:40 AM
Long-term archiving on: : Wednesday, April 8, 2020 - 12:00:22 AM

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Jean-Daniel Boissonnat, Tamal K. Dey, Clément Maria. The Compressed Annotation Matrix: an Efficient Data Structure for Computing Persistent Cohomology. Algorithmica, Springer Verlag, In press, 73 (3), pp.14. ⟨10.1007/s00453-015-9999-4⟩. ⟨hal-00761468v4⟩

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