| dc.contributor.author | Ramírez García, David | |
| dc.contributor.author | Santamaría Caballero, Luis Ignacio | |
| dc.contributor.author | Vaerenbergh, Steven van | |
| dc.contributor.author | Scharf, Louis L. | |
| dc.contributor.other | Universidad de Cantabria | es_ES |
| dc.date.accessioned | 2019-03-13T16:07:27Z | |
| dc.date.available | 2019-03-13T16:07:27Z | |
| dc.date.issued | 2018 | |
| dc.identifier.isbn | 978-1-5386-9218-9 | |
| dc.identifier.other | TEC2013-41718-R | es_ES |
| dc.identifier.other | TEC2015-69648-REDC | es_ES |
| dc.identifier.other | TEC2015-69868-C2-1-R | es_ES |
| dc.identifier.other | TEC2017-86921-C2-2-R | es_ES |
| dc.identifier.other | TEC2016-75067-C4-4-R | es_ES |
| dc.identifier.uri | http://hdl.handle.net/10902/15871 | |
| dc.description.abstract | An alternating optimization algorithm is presented and analyzed for identifying low-rank signal components, known in factor analysis terminology as common factors, that are correlated across two multiple-input multiple-output (MIMO) channels. The additive noise model at each of the MIMO channels consists of white uncorrelated noises of unequal variances plus a low-rank structured interference that is not correlated across the two channels. The low-rank components at each channel represent uncommon or channel-specific factors. | es_ES |
| dc.description.sponsorship | The work of D. Ram´ırez was supported by the Ministerio de Economía, Industria y Competitividad (MINECO) and AEI/FEDER funds of the E.U., under grants TEC2013- 41718-R (OTOSIS), TEC2015-69648-REDC (COMONSENS Network), TEC2015-69868-C2-1-R (ADVENTURE), and CAIMAN (TEC2017-86921-C2-2-R) and The Comunidad de Madrid under grant S2013/ICE-2845 (CASI-CAM-CM). The work of I. Santamaria and S. Van Vaerenbergh was supported by MINECO and AEI/FEDER funds of the E.U., under grant TEC2016-75067-C4-4-R (CARMEN). The work of L. Scharf was supported in part by the National Science Foundation under grant CCF-1712788. | es_ES |
| dc.format.extent | 5 p. | es_ES |
| dc.language.iso | eng | es_ES |
| dc.publisher | IEEE | es_ES |
| dc.rights | © 2018 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 | 52nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, 2018, 1743-1747 | es_ES |
| dc.subject.other | Factor analysis | es_ES |
| dc.subject.other | Expectation-maximization | es_ES |
| dc.subject.other | Maximum likelihood | es_ES |
| dc.subject.other | MIMO channels | es_ES |
| dc.subject.other | Multivariate normal model | es_ES |
| dc.title | An alternating optimization algorithm for two-channel factor analysis with common and uncommon factors | es_ES |
| dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
| dc.relation.publisherVersion | https://doi.org/10.1109/ACSSC.2018.8645457 | es_ES |
| dc.rights.accessRights | openAccess | es_ES |
| dc.identifier.DOI | 10.1109/ACSSC.2018.8645457 | |
| dc.type.version | acceptedVersion | es_ES |
