Pith. sign in

REVIEW 1 cited by

Updating Barcodes and Representatives for Zigzag Persistence

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2112.02352 v2 pith:5MKAVITL submitted 2021-12-04 cs.CG math.AT

classification cs.CGmath.AT
keywords zigzagnon-zigzagpersistencebarcodeschangingcomputingoperationsrepresentatives
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Computing persistence over changing filtrations give rise to a stack of 2D persistence diagrams where the birth-death points are connected by the so-called `vines'. We consider computing these vines over changing filtrations for zigzag persistence. We observe that eight atomic operations are sufficient for changing one zigzag filtration to another and provide update algorithms for each of them. Six of these operations that have some analogues to one or multiple transpositions in the non-zigzag case can be executed as efficiently as their non-zigzag counterparts. This approach takes advantage of a recently discovered algorithm for computing zigzag barcodes by converting a zigzag filtration to a non-zigzag one and then connecting barcodes of the two with a bijection. The remaining two atomic operations do not have a strict analogue in the non-zigzag case. For them, we propose algorithms based on explicit maintenance of representatives (homology cycles) which can be useful in their own rights for applications requiring explicit updates of representatives.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Topological Data Analysis and Topological Deep Learning Beyond Persistent Homology -- A Review

    math.HO 2025-07 conditional novelty 3.0 of 10

    A survey organizing recent TDA and TDL methods beyond persistent homology and connecting them to data structures and vectorization.

Pith tools