Koka, Taulant ; Muma, Michael ; Haro, Benjamín Béjar (2022)
Reconstruction of Multivariate Sparse Signals from Mismatched Samples.
doi: 10.48550/ARXIV.2212.07368
Report, Bibliographie

Kurzbeschreibung (Abstract)

Erroneous correspondences between samples and their respective channel or target commonly arise in several real-world applications. For instance, whole-brain calcium imaging of freely moving organisms, multiple target tracking or multi-person contactless vital sign monitoring may be severely affected by mismatched sample-channel assignments. To systematically address this fundamental problem, we pose it as a signal reconstruction problem where we have lost correspondences between the samples and their respective channels. We show that under the assumption that the signals of interest admit a sparse representation over an overcomplete dictionary, unique signal recovery is possible. Our derivations reveal that the problem is equivalent to a structured unlabeled sensing problem without precise knowledge of the sensing matrix. Unfortunately, existing methods are neither robust to errors in the regressors nor do they exploit the structure of the problem. Therefore, we propose a novel robust two-step approach for the reconstruction of shuffled sparse signals. The performance and robustness of the proposed approach is illustrated in an application of whole-brain calcium imaging in computational neuroscience. The proposed framework can be generalized to sparse signal representations other than the ones considered in this work to be applied in a variety of real-world problems with imprecise measurement or channel assignment.

Typ des Eintrags:	Report
Erschienen:	2022
Autor(en):	Koka, Taulant ; Muma, Michael ; Haro, Benjamín Béjar
Art des Eintrags:	Bibliographie
Titel:	Reconstruction of Multivariate Sparse Signals from Mismatched Samples
Sprache:	Englisch
Publikationsjahr:	2022
Verlag:	arXiv
(Heft-)Nummer:	Preprint
Reihe:	Electrical Engineering and Systems Science
Auflage:	1.Version
DOI:	10.48550/ARXIV.2212.07368
URL / URN:	https://arxiv.org/abs/2212.07368
Zugehörige Links:	Verwandtes Werk
Kurzbeschreibung (Abstract):	Erroneous correspondences between samples and their respective channel or target commonly arise in several real-world applications. For instance, whole-brain calcium imaging of freely moving organisms, multiple target tracking or multi-person contactless vital sign monitoring may be severely affected by mismatched sample-channel assignments. To systematically address this fundamental problem, we pose it as a signal reconstruction problem where we have lost correspondences between the samples and their respective channels. We show that under the assumption that the signals of interest admit a sparse representation over an overcomplete dictionary, unique signal recovery is possible. Our derivations reveal that the problem is equivalent to a structured unlabeled sensing problem without precise knowledge of the sensing matrix. Unfortunately, existing methods are neither robust to errors in the regressors nor do they exploit the structure of the problem. Therefore, we propose a novel robust two-step approach for the reconstruction of shuffled sparse signals. The performance and robustness of the proposed approach is illustrated in an application of whole-brain calcium imaging in computational neuroscience. The proposed framework can be generalized to sparse signal representations other than the ones considered in this work to be applied in a variety of real-world problems with imprecise measurement or channel assignment.
Freie Schlagworte:	Signal Processing (eess.SP), Machine Learning (cs.LG), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences
Zusätzliche Informationen:	Publikation wurde mit 3.Version geändert (24.07.2023), neuer Titel: Shuffled Multi-Channel Sparse Signal Recovery; siehe "zugehörige Links"
Fachbereich(e)/-gebiet(e):	18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Robust Data Science
Hinterlegungsdatum:	19 Dez 2022 09:59
Letzte Änderung:	26 Jul 2023 08:51
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Verfügbare Versionen dieses Eintrags

• Reconstruction of Multivariate Sparse Signals from Mismatched Samples. (deposited 19 Dez 2022 09:59) [Gegenwärtig angezeigt]
  • Shuffled Multi-Channel Sparse Signal Recovery. (deposited 26 Jul 2023 08:51)

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