| dc.contributor.author | Stanton, Gray | |
| dc.contributor.author | Ramírez García, David | |
| dc.contributor.author | Santamaría Caballero, Luis Ignacio | |
| dc.contributor.author | Scharf, Louis L. | |
| dc.contributor.author | Wang, Haonan | |
| dc.contributor.other | Universidad de Cantabria | es_ES |
| dc.date.accessioned | 2024-04-16T07:54:01Z | |
| dc.date.available | 2024-04-16T07:54:01Z | |
| dc.date.issued | 2023 | |
| dc.identifier.isbn | 979-8-3503-2574-4 | |
| dc.identifier.other | PID2022-137099NB-C43 | es_ES |
| dc.identifier.other | PID2021-1231820B-I00 | es_ES |
| dc.identifier.uri | https://hdl.handle.net/10902/32589 | |
| dc.description.abstract | The recently developed Multi-Channel Factor Anal ysis (MFA) is a method for extracting a latent low-dimensional signal that is present across multiple channels and corrupted by unobserved single-channel interference and idiosyncratic noise. In MFA, only the channel structure and dimensionality of the signal and interference subspaces are specified in advance, which raises the concern that the signal, interference, and noise covariances may not be uniquely determined by the observation model. This paper presents necessary and sufficient conditions on the channel sizes and subspace dimensions to guarantee the identifiability of MFA, ensuring that the second-order spatial properties of the latent components can, in principle, be recovered from the multi-channel observations. | es_ES |
| dc.description.sponsorship | This work was supported in part by National Science Foundation grants DMS-1923142, CNS-1932413, and DMS 2123761. The work of I. Santamaria was funded by MCIN/ AEI /10.13039/501100011033, under grant PID2022-137099NB C43 (MADDIE). The work of D. Ramírez was partially supported by MCIN/AEI/10.13039/501100011033/FEDER, UE, under grant PID2021-123182OB-I00 (EPiCENTER) and by the Office of Naval Research (ONR) Global under contract N62909-23-1-2002. | es_ES |
| dc.format.extent | 6 p. | es_ES |
| dc.language.iso | eng | es_ES |
| dc.publisher | Institute of Electrical and Electronics Engineers, Inc. | es_ES |
| dc.rights | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | es_ES |
| dc.source | 57th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, 2023, 1344-1349 | es_ES |
| dc.subject.other | Factor analysis (FA) | es_ES |
| dc.subject.other | Identifiability | es_ES |
| dc.subject.other | Multi-channel factor analysis (MFA) | es_ES |
| dc.title | Identifiability in multi-channel factor analysis | es_ES |
| dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
| dc.relation.publisherVersion | https://doi.org/10.1109/IEEECONF59524.2023.10476820 | es_ES |
| dc.rights.accessRights | openAccess | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-137099NB-C43/ES/TECNOLOGIAS DE COMUNICACION, CODIFICACION Y PROCESADO PARA REDES CLASICAS-CUANTICAS DE PROXIMA GENERACION/ | es_ES |
| dc.identifier.DOI | 10.1109/IEEECONF59524.2023.10476820 | |
| dc.type.version | acceptedVersion | es_ES |
