Fast Data-Driven Greedy Sensor Selection for Ridge Regression

We propose a data-driven sensor-selection algorithm for accurate estimation of the target variables from the selected measurements. The target variables are assumed to be estimated by a ridge-regression estimator, which is trained based on the data. The proposed algorithm greedily selects sensors fo...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE sensors journal Jg. 25; H. 6; S. 10030 - 10045
Hauptverfasser:	Sasaki, Yasuo, Yamada, Keigo, Nagata, Takayuki, Saito, Yuji, Nonomura, Taku
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 15.03.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Algorithms Computational modeling Cost function Datasets Estimation Greedy algorithms Intelligent sensors Keywords Greedy algorithm Linear programming Linear systems Overfitting Parameters Regularization ridge regression sensor selection Sensors Training Vectors
ISSN:	1530-437X, 1558-1748
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	We propose a data-driven sensor-selection algorithm for accurate estimation of the target variables from the selected measurements. The target variables are assumed to be estimated by a ridge-regression estimator, which is trained based on the data. The proposed algorithm greedily selects sensors for minimizing the cost function of the estimator. Sensor selection that prevents overfitting of the resulting estimator can be realized by setting a positive regularization parameter. The greedy solution is computed in quite a short time by using some recurrent relations that we derive. The effectiveness of the proposed algorithm is verified for artificial datasets that are generated from linear systems and a real-wold dataset that is aimed for the selection of pressure-sensor locations for estimating the yaw angle of a ground vehicle. The demonstration for the datasets reveals that the proposed algorithm computes a sensor set, resulting in more accurate estimation than existing data-driven selection algorithms in some conditions. Furthermore, it is confirmed that setting a positive regularization parameter in the proposed algorithm leads to accurate estimation when overfitting is problematic.
AbstractList	We propose a data-driven sensor-selection algorithm for accurate estimation of the target variables from the selected measurements. The target variables are assumed to be estimated by a ridge-regression estimator, which is trained based on the data. The proposed algorithm greedily selects sensors for minimizing the cost function of the estimator. Sensor selection that prevents overfitting of the resulting estimator can be realized by setting a positive regularization parameter. The greedy solution is computed in quite a short time by using some recurrent relations that we derive. The effectiveness of the proposed algorithm is verified for artificial datasets that are generated from linear systems and a real-wold dataset that is aimed for the selection of pressure-sensor locations for estimating the yaw angle of a ground vehicle. The demonstration for the datasets reveals that the proposed algorithm computes a sensor set, resulting in more accurate estimation than existing data-driven selection algorithms in some conditions. Furthermore, it is confirmed that setting a positive regularization parameter in the proposed algorithm leads to accurate estimation when overfitting is problematic.
Author	Yamada, Keigo Nonomura, Taku Nagata, Takayuki Saito, Yuji Sasaki, Yasuo
Author_xml	– sequence: 1 givenname: Yasuo orcidid: 0000-0003-0450-2764 surname: Sasaki fullname: Sasaki, Yasuo email: sasaki.yasuo.g8@f.mail.nagoya-u.ac.jp organization: Department of Aerospace Engineering, Nagoya University, Nagoya, Japan – sequence: 2 givenname: Keigo orcidid: 0000-0001-8399-9574 surname: Yamada fullname: Yamada, Keigo email: keigo.yamada.t5@dc.tohoku.ac.jp organization: Department of Aerospace Engineering, Tohoku University, Sendai, Japan – sequence: 3 givenname: Takayuki orcidid: 0000-0003-3644-4888 surname: Nagata fullname: Nagata, Takayuki email: takayuki.nagata@mae.nagoya-u.ac.jp organization: Department of Aerospace Engineering, Nagoya University, Nagoya, Japan – sequence: 4 givenname: Yuji orcidid: 0000-0003-2804-8076 surname: Saito fullname: Saito, Yuji email: yuji.saito@tohoku.ac.jp organization: Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, Sendai, Japan – sequence: 5 givenname: Taku surname: Nonomura fullname: Nonomura, Taku email: nonomura@nagoya-u.jp organization: Department of Aerospace Engineering, Nagoya University, Nagoya, Japan
BookMark	eNp9kE1Lw0AQhhepYFv9AYKHgOfU2WyS2Rw8SL9UikKr4G2ZJhOJ1KTupkL_vRvag3jwNB-8z3y8A9Grm5qFuJQwkhKym8fV9GkUQZSMVKIQIToRfZkkOpQY616XKwhjhW9nYuDcB4DMMMG-uJ2Ra4MJtRRObPXNdTC3zMU-WHHtGuvDhvO2auqg9NWyKt45WPK7Zed881yclrRxfHGMQ_E6m76M78PF8_xhfLcI8yhL21AD8BqiNGOigigGBSkXsVQ5lIhrrSElxERnhDrPdCoL7as1lOT1GbEaiuvD3K1tvnbsWvPR7GztVxolEaPu8cSr8KDKbeOc5dLkVUvd8a2lamMkmM4r03llOsQcvfKk_ENubfVJdv8vc3VgKmb-pdeoVazVD6e7ddA
CODEN	ISJEAZ
CitedBy_id	crossref_primary_10_1109_LSENS_2025_3591066
Cites_doi	10.1109/JSEN.2023.3258223 10.1109/CDC.2010.5717225 10.1109/JSEN.2020.3013094 10.1109/TSP.2008.2007095 10.1016/0893-6080(91)90009-T 10.1093/oso/9780199296590.001.0001 10.1109/JSEN.2018.2887044 10.1109/ACCESS.2022.3194250 10.1007/978-3-030-68056-5 10.1145/1374376.1374384 10.1109/TAC.2021.3082502 10.1177/1475921719825601 10.1145/1791212.1791238 10.26868/25222708.2019.210916 10.1109/TSP.2016.2550005 10.1016/j.ymssp.2022.109957 10.3390/app11094216 10.1016/j.automatica.2016.12.025 10.1109/TSP.2019.2903017 10.1088/1748-9326/ac548f 10.1016/j.ymssp.2021.107619 10.1016/j.sigpro.2005.05.030 10.1109/TSP.2016.2573767 10.2322/tjsass.64.242 10.1016/j.ensm.2022.06.053 10.1007/s10115-012-0538-1 10.1109/TAC.2013.2257618 10.1109/TSP.2021.3063495 10.1214/17-AOS1679 10.1109/JSEN.2021.3073978 10.1007/s00332-022-09806-9 10.1201/9781420035933 10.1109/TIT.2004.834793 10.1109/TAC.2014.2351673 10.32604/cmes.2021.016603 10.1109/MCS.2018.2810460 10.1109/LCSYS.2018.2849611 10.1109/LSP.2014.2342198 10.1007/s00348-022-03471-0 10.1109/ACCESS.2021.3076186 10.1109/JSTSP.2009.2028381 10.1093/gji/ggad165 10.23919/ECC.2019.8795800 10.1109/TCNS.2015.2453711 10.1017/CBO9781139177801.004 10.1007/s12650-022-00855-6 10.1109/TSP.2022.3224643 10.1137/S0097539792240406 10.1109/TKDE.2014.2349910 10.1109/TSP.2022.3212150 10.1109/LSENS.2020.2999186 10.1109/CDC42340.2020.9304166 10.2514/6.2023-1944 10.1109/TC.2015.2395423 10.23919/ACC.2019.8814464 10.23919/ACC.2017.7962975 10.1109/ISIC.2016.7579985 10.1109/ACSSC.1993.342465 10.1016/j.jweia.2022.105043 10.1063/5.0049071 10.1007/BF01588971 10.1109/JSEN.2023.3328005 10.1109/ACCESS.2023.3291415 10.1109/TSP.2014.2379662 10.1109/CDC.2018.8619761 10.1109/TAC.2020.2973774 10.1109/ACCESS.2021.3067712 10.1109/TAC.2020.3044284 10.1109/BCI51272.2021.9385301 10.3390/s23135961 10.1109/LSP.2021.3050708 10.1109/TSP.2014.2299518 10.1093/gji/ggac443 10.4271/840300 10.1017/jfm.2021.948 10.1109/CDC.2014.7040017 10.1016/j.compchemeng.2012.05.010 10.1109/ISCIT.2009.5341219 10.1007/978-1-4615-7566-5
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
DBID	97E RIA RIE AAYXX CITATION 7SP 7U5 8FD L7M
DOI	10.1109/JSEN.2025.3537702
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Solid State and Superconductivity Abstracts Technology Research Database Advanced Technologies Database with Aerospace
DatabaseTitle	CrossRef Solid State and Superconductivity Abstracts Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts
DatabaseTitleList	Solid State and Superconductivity Abstracts
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Geography Engineering
EISSN	1558-1748
EndPage	10045
ExternalDocumentID	10_1109_JSEN_2025_3537702 10878348
Genre	orig-research
GrantInformation_xml	– fundername: Japan Society of the Promotion of Science KAKENHI grantid: 21J14180; 23K13348 funderid: 10.13039/501100000646 – fundername: JST CREST grantid: JPMJCR1763 – fundername: Japan Science and Technology Agency (JST) Moonshot Research and Development Program grantid: JPMJMS2287 funderid: 10.13039/501100013352
GroupedDBID	-~X 0R~ 29I 4.4 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AGQYO AHBIQ AJQPL AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 EBS F5P HZ~ IFIPE IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS TWZ AAYXX CITATION 7SP 7U5 8FD L7M
ID	FETCH-LOGICAL-c296t-800eb0269eaadaa40306ed413c0f77b8806a77589a78c9861d8758b0faaad9ae3
IEDL.DBID	RIE
ISICitedReferencesCount	1
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001445066200005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1530-437X
IngestDate	Mon Jun 30 10:01:26 EDT 2025 Tue Nov 18 21:02:35 EST 2025 Sat Nov 29 08:04:40 EST 2025 Wed Aug 27 01:42:14 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	6
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c296t-800eb0269eaadaa40306ed413c0f77b8806a77589a78c9861d8758b0faaad9ae3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-8399-9574 0000-0003-3644-4888 0000-0003-0450-2764 0000-0003-2804-8076
PQID	3177220255
PQPubID	75733
PageCount	16
ParticipantIDs	crossref_citationtrail_10_1109_JSEN_2025_3537702 crossref_primary_10_1109_JSEN_2025_3537702 ieee_primary_10878348 proquest_journals_3177220255
PublicationCentury	2000
PublicationDate	2025-03-15
PublicationDateYYYYMMDD	2025-03-15
PublicationDate_xml	– month: 03 year: 2025 text: 2025-03-15 day: 15
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE sensors journal
PublicationTitleAbbrev	JSEN
PublicationYear	2025
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref57 ref12 ref56 ref15 ref59 Das (ref76) ref14 ref58 ref53 ref52 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 ref51 ref50 ref46 ref45 Yeo (ref2) 2022 ref48 ref47 ref42 ref41 ref85 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref82 ref81 ref40 ref84 ref83 ref80 ref35 ref79 ref34 ref78 ref37 ref36 ref31 ref75 ref30 ref74 ref33 ref32 Das (ref77) 2018; 19 ref1 ref38 ref71 ref70 ref73 ref72 Micchelli (ref67) 2006; 7 ref24 ref68 ref26 ref25 ref69 ref20 ref64 ref63 ref22 ref66 ref21 ref65 ref28 ref27 ref29 ref60 ref62 ref61 Gower (ref23) Krause (ref39) 2008; 9
References_xml	– ident: ref50 doi: 10.1109/JSEN.2023.3258223 – ident: ref41 doi: 10.1109/CDC.2010.5717225 – volume: 7 start-page: 2651 year: 2006 ident: ref67 article-title: Universal kernels publication-title: J. Mach. Learn. Res. – ident: ref54 doi: 10.1109/JSEN.2020.3013094 – ident: ref22 doi: 10.1109/TSP.2008.2007095 – ident: ref68 doi: 10.1016/0893-6080(91)90009-T – ident: ref80 doi: 10.1093/oso/9780199296590.001.0001 – ident: ref53 doi: 10.1109/JSEN.2018.2887044 – ident: ref60 doi: 10.1109/ACCESS.2022.3194250 – ident: ref83 doi: 10.1007/978-3-030-68056-5 – ident: ref72 doi: 10.1145/1374376.1374384 – volume: 9 start-page: 235 year: 2008 ident: ref39 article-title: Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies publication-title: J. Mach. Learn. Res. – ident: ref37 doi: 10.1109/TAC.2021.3082502 – ident: ref14 doi: 10.1177/1475921719825601 – ident: ref18 doi: 10.1145/1791212.1791238 – ident: ref65 doi: 10.26868/25222708.2019.210916 – ident: ref20 doi: 10.1109/TSP.2016.2550005 – ident: ref9 doi: 10.1016/j.ymssp.2022.109957 – ident: ref4 doi: 10.3390/app11094216 – ident: ref16 doi: 10.1016/j.automatica.2016.12.025 – ident: ref48 doi: 10.1109/TSP.2019.2903017 – ident: ref10 doi: 10.1088/1748-9326/ac548f – ident: ref58 doi: 10.1016/j.ymssp.2021.107619 – ident: ref78 doi: 10.1016/j.sigpro.2005.05.030 – ident: ref35 doi: 10.1109/TSP.2016.2573767 – ident: ref5 doi: 10.2322/tjsass.64.242 – ident: ref66 doi: 10.1016/j.ensm.2022.06.053 – ident: ref61 doi: 10.1007/s10115-012-0538-1 – ident: ref28 doi: 10.1109/TAC.2013.2257618 – ident: ref62 doi: 10.1109/TSP.2021.3063495 – ident: ref75 doi: 10.1214/17-AOS1679 – ident: ref32 doi: 10.1109/JSEN.2021.3073978 – ident: ref40 doi: 10.1007/s00332-022-09806-9 – ident: ref69 doi: 10.1201/9781420035933 – ident: ref71 doi: 10.1109/TIT.2004.834793 – ident: ref29 doi: 10.1109/TAC.2014.2351673 – ident: ref59 doi: 10.32604/cmes.2021.016603 – ident: ref52 doi: 10.1109/MCS.2018.2810460 – ident: ref25 doi: 10.1109/LCSYS.2018.2849611 – ident: ref30 doi: 10.1109/LSP.2014.2342198 – ident: ref6 doi: 10.1007/s00348-022-03471-0 – ident: ref55 doi: 10.1109/ACCESS.2021.3076186 – ident: ref3 doi: 10.1109/JSTSP.2009.2028381 – ident: ref13 doi: 10.1093/gji/ggad165 – ident: ref44 doi: 10.23919/ECC.2019.8795800 – ident: ref43 doi: 10.1109/TCNS.2015.2453711 – ident: ref74 doi: 10.1017/CBO9781139177801.004 – ident: ref84 doi: 10.1007/s12650-022-00855-6 – ident: ref49 doi: 10.1109/TSP.2022.3224643 – ident: ref15 doi: 10.1137/S0097539792240406 – ident: ref79 doi: 10.1109/TKDE.2014.2349910 – ident: ref33 doi: 10.1109/TSP.2022.3212150 – ident: ref56 doi: 10.1109/LSENS.2020.2999186 – ident: ref45 doi: 10.1109/CDC42340.2020.9304166 – ident: ref81 doi: 10.2514/6.2023-1944 – ident: ref11 doi: 10.1109/TC.2015.2395423 – ident: ref36 doi: 10.23919/ACC.2019.8814464 – ident: ref19 doi: 10.23919/ACC.2017.7962975 – start-page: 1057 volume-title: Proc. 28th Int. Conf. Mach. Learn. ident: ref76 article-title: Submodular meets spectral: Greedy algorithms for subset selection, sparse approximation and dictionary selection – ident: ref26 doi: 10.1109/ISIC.2016.7579985 – year: 2022 ident: ref2 article-title: Effcient magnetometer sensor array selection for signal reconstruction and brain source localization publication-title: arXiv:2205.10925 – ident: ref70 doi: 10.1109/ACSSC.1993.342465 – ident: ref63 doi: 10.1016/j.jweia.2022.105043 – ident: ref8 doi: 10.1063/5.0049071 – start-page: 616 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref23 article-title: RSN: Randomized subspace Newton – ident: ref73 doi: 10.1007/BF01588971 – ident: ref51 doi: 10.1109/JSEN.2023.3328005 – ident: ref38 doi: 10.1109/ACCESS.2023.3291415 – ident: ref27 doi: 10.1109/TSP.2014.2379662 – ident: ref34 doi: 10.1109/CDC.2018.8619761 – ident: ref46 doi: 10.1109/TAC.2020.2973774 – ident: ref57 doi: 10.1109/ACCESS.2021.3067712 – ident: ref47 doi: 10.1109/TAC.2020.3044284 – ident: ref85 doi: 10.1109/BCI51272.2021.9385301 – ident: ref21 doi: 10.3390/s23135961 – ident: ref24 doi: 10.1109/LSP.2021.3050708 – ident: ref42 doi: 10.1109/TSP.2014.2299518 – ident: ref12 doi: 10.1093/gji/ggac443 – ident: ref82 doi: 10.4271/840300 – ident: ref7 doi: 10.1017/jfm.2021.948 – ident: ref31 doi: 10.1109/CDC.2014.7040017 – volume: 19 start-page: 74 issue: 1 year: 2018 ident: ref77 article-title: Approximate submodularity and its applications: Subset selection, sparse approximation and dictionary selection publication-title: J. Mach. Learn. Res. – ident: ref1 doi: 10.1016/j.compchemeng.2012.05.010 – ident: ref17 doi: 10.1109/ISCIT.2009.5341219 – ident: ref64 doi: 10.1007/978-1-4615-7566-5
SSID	ssj0019757
Score	2.4300492
Snippet	We propose a data-driven sensor-selection algorithm for accurate estimation of the target variables from the selected measurements. The target variables are...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	10030
SubjectTerms	Accuracy Algorithms Computational modeling Cost function Datasets Estimation Greedy algorithms Intelligent sensors Keywords Greedy algorithm Linear programming Linear systems Overfitting Parameters Regularization ridge regression sensor selection Sensors Training Vectors
Title	Fast Data-Driven Greedy Sensor Selection for Ridge Regression
URI	https://ieeexplore.ieee.org/document/10878348 https://www.proquest.com/docview/3177220255
Volume	25
WOSCitedRecordID	wos001445066200005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-1748 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0019757 issn: 1530-437X databaseCode: RIE dateStart: 20010101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB50EdSDb3F90YMnoZq-8jh4EHURkUV8wN5K2kxVWLqyWwX_vZM0Kwui4KmFzkCZyWTyZV4ARyaTFSLZdxEnPExNwUNJjj7MIkksKLlC1zL_VvT7cjBQd75Y3dXCIKJLPsMT--pi-WZUvturMrJwaedCyHmYF4K3xVrfIQMlXFtPsmAWpokY-BBmxNTpzcNVn6BgnJ0kWSKEv0KZOiE3VeXHVuz8S2_1n3-2Biv-IBmct5pfhzmsN2B5pr3gBiz6Cecvn5tw1tOTJrjUjQ4vx3aHC2zGjfkMHgjHjsb0GLqkrDqgU2xwb6u4gnt8brNk6y146l09XlyHfnRCWMaKN-R3GBYErxRqbbROLTJAQw6rZJUQBRkt14KggtJClkryyBBukQWrNNErjck2dOpRjTsQlBUXpDMjdJykrIqKlGciZgUmKBJTZl1gU1nmpe8rbsdbDHOHL5jKrfhzK_7ci78Lx98sb21Tjb-It6y8ZwhbUXdhf6qx3NvdJKfTkIgtd7b7C9seLNnvNo0syvah04zf8QAWyo_mdTI-dEvqC6dExyA
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1La9wwEB7ygrSHpnmUbLtpfOgp4FR-yJIOPZRml6TZmpJNYG9GtsZtIXiL1wnk33cka5dASCEn-zADZsajmU_zAvhkuKwRyb7LOMnC1JRZKMnRhzySxIIyU-hG5k9EnsvZTP30zequFwYRXfEZntpXl8s38-rOXpWRhUu7F0KuwyZP05j17VqrpIESbrAn2TAL00TMfBIzYurz9-koJzAY89OEJ0L4S5SlG3J7VZ4cxs7DjHde-G1v4Y0PJYOvve53YQ2bPXj9aMDgHmz7Hee_H_bhy1gvuuBMdzo8a-0ZF9iaG_MQTAnJzlt63LqyrCagODa4sn1cwRX-6utkmwO4GY-uv52HfnlCWMUq68jzMCwJYCnU2midWmyAhlxWxWohSjLbTAsCC0oLWSmZRYaQiyxZrYleaUzewUYzb_AQgqrOBGnNCB0nKaujMs24iFmJCYrEVHwAbCnLovKTxe2Ci9vCIQymCiv-woq_8OIfwMmK5W8_VuN_xAdW3o8Ie1EPYLjUWOEtb1FQPCRiy83fP8N2DNvn1z8mxeQiv_wAryytLSqL-BA2uvYOj2Cruu_-LNqP7vf6BzAsymc
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Fast+Data-Driven+Greedy+Sensor+Selection+for+Ridge+Regression&rft.jtitle=IEEE+sensors+journal&rft.au=Sasaki%2C+Yasuo&rft.au=Yamada%2C+Keigo&rft.au=Nagata%2C+Takayuki&rft.au=Saito%2C+Yuji&rft.date=2025-03-15&rft.issn=1530-437X&rft.eissn=1558-1748&rft.volume=25&rft.issue=6&rft.spage=10030&rft.epage=10045&rft_id=info:doi/10.1109%2FJSEN.2025.3537702&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_JSEN_2025_3537702
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1530-437X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1530-437X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1530-437X&client=summon