TY - GEN
T1 - Signed Barcodes for Multi-Parameter Persistence via Rank Decompositions
AU - Botnan, Magnus Bakke
AU - Oppermann, Steffen
AU - Oudot, Steve
N1 - Publisher Copyright:
© Magnus Bakke Botnan, Steffen Oppermann, and Steve Oudot; licensed under Creative Commons License CC-BY 4.0
PY - 2022/6/1
Y1 - 2022/6/1
N2 - In this paper we introduce the signed barcode, a new visual representation of the global structure of the rank invariant of a multi-parameter persistence module or, more generally, of a poset representation. Like its unsigned counterpart in one-parameter persistence, the signed barcode encodes the rank invariant as a Z-linear combination of rank invariants of indicator modules supported on segments in the poset. It can also be enriched to encode the generalized rank invariant as a Z-linear combination of generalized rank invariants in fixed classes of interval modules. In the paper we develop the theory behind these rank decompositions, showing under what conditions they exist and are unique - so the signed barcode is canonically defined. We also illustrate the contribution of the signed barcode to the exploration of multi-parameter persistence modules through a practical example.
AB - In this paper we introduce the signed barcode, a new visual representation of the global structure of the rank invariant of a multi-parameter persistence module or, more generally, of a poset representation. Like its unsigned counterpart in one-parameter persistence, the signed barcode encodes the rank invariant as a Z-linear combination of rank invariants of indicator modules supported on segments in the poset. It can also be enriched to encode the generalized rank invariant as a Z-linear combination of generalized rank invariants in fixed classes of interval modules. In the paper we develop the theory behind these rank decompositions, showing under what conditions they exist and are unique - so the signed barcode is canonically defined. We also illustrate the contribution of the signed barcode to the exploration of multi-parameter persistence modules through a practical example.
KW - Topological data analysis
KW - multi-parameter persistent homology
UR - https://www.scopus.com/pages/publications/85134343980
U2 - 10.4230/LIPIcs.SoCG.2022.19
DO - 10.4230/LIPIcs.SoCG.2022.19
M3 - Conference contribution
AN - SCOPUS:85134343980
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 38th International Symposium on Computational Geometry, SoCG 2022
A2 - Goaoc, Xavier
A2 - Kerber, Michael
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
T2 - 38th International Symposium on Computational Geometry, SoCG 2022
Y2 - 7 June 2022 through 10 June 2022
ER -