Simplicial Trend Filtering (Invited Paper)

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)
7 Downloads (Pure)

Abstract

Reconstructing simplicial signals, e.g., signals defined on nodes, edges, triangles, etc., of a network, from (partial) noisy observation is of interest in water/traffic flow estimation or currency exchange markets. Typically, this concerns solving a regularised problem w.r.t. the l2 norm of the divergence or the curl of the signal, i.e., the netflows at nodes and in triangles. Realworld simplicial signals are intrinsically divergence- or curl-free, which makes l2 regularizers inapplicable. To overcome this, we develop a simplicial trend filter (STF) by regularising the total divergence and the curl via their l1 norm. By tuning two scalars, the STF can reduce independently the divergence and curl much more than smooth filtering, leading to a better reconstructed signal. The SFT is a convex problem and can be solved by fast iterative algorithms. We apply the SFT to interpolation and denoising tasks in forex and music/artist transition recordings and show its superior performance to alternatives.

Original languageEnglish
Title of host publicationProceedings of the 56th Asilomar Conference on Signals, Systems and Computers
EditorsMichael B. Matthews
PublisherIEEE
Pages930-934
Number of pages5
ISBN (Electronic)978-1-6654-5906-8
ISBN (Print)978-1-6654-5907-5
DOIs
Publication statusPublished - 2022
Event56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 - Virtual, Online, United States
Duration: 31 Oct 20222 Nov 2022

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2022-October
ISSN (Print)1058-6393

Conference

Conference56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
Country/TerritoryUnited States
CityVirtual, Online
Period31/10/222/11/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Funding Information:
Emails: m.yang-2, e.isufi-1@tudelft.nl. This work is supported by the TU Delft AI Labs Programme.

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