Low-rank elastic-net regularized multivariate Huber regression model
Heavy-tailed noise or strongly correlated predictors often go with the multivariate linear regression model. To tackle with these problems, this paper focuses on the matrix elastic-net regularized multivariate Huber regression model. This new model possesses the grouping effect property and the robu...
Uloženo v:
| Vydáno v: | Applied Mathematical Modelling Ročník 87; s. 571 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Elsevier BV
01.11.2020
|
| Témata: | |
| ISSN: | 1088-8691, 0307-904X |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Heavy-tailed noise or strongly correlated predictors often go with the multivariate linear regression model. To tackle with these problems, this paper focuses on the matrix elastic-net regularized multivariate Huber regression model. This new model possesses the grouping effect property and the robustness to heavy-tailed noise. Meanwhile, it also has the ability of reducing the negative effect of outliers due to Huber loss. Furthermore, an accelerated proximal gradient algorithm is designed to solve the proposed model. Some numerical studies including a real data analysis are dedicated to show the efficiency of our method. |
|---|---|
| AbstractList | Heavy-tailed noise or strongly correlated predictors often go with the multivariate linear regression model. To tackle with these problems, this paper focuses on the matrix elastic-net regularized multivariate Huber regression model. This new model possesses the grouping effect property and the robustness to heavy-tailed noise. Meanwhile, it also has the ability of reducing the negative effect of outliers due to Huber loss. Furthermore, an accelerated proximal gradient algorithm is designed to solve the proposed model. Some numerical studies including a real data analysis are dedicated to show the efficiency of our method. |
| Author | Zhai, Wenjuan Huang, Zhiyong Chen, Bingzhen |
| Author_xml | – sequence: 1 givenname: Bingzhen surname: Chen fullname: Chen, Bingzhen – sequence: 2 givenname: Wenjuan surname: Zhai fullname: Zhai, Wenjuan – sequence: 3 givenname: Zhiyong surname: Huang fullname: Huang, Zhiyong |
| BookMark | eNotjc1KxDAYRYOMYGf0AdwVXKd-SZMmXcr4M0LBjYK7IT9fpbVNx6RV8Okd0dXlci7nrskqTAEJuWRQMGDVdV-Yw1hw4FCALIDxE5JBCYrWIF5XJGOgNdVVzc7IOqUejgtdQUZum-mLRhPecxxMmjtHA855xLdlMLH7Rp-PyzB3n8diZsx3i8X4iyOm1E0hHyePwzk5bc2Q8OI_N-Tl_u55u6PN08Pj9qahruRypi1UxmkujOWqQm-98NZogV555nSN2ggGHHkrWSss2rpVLThvmVRCWuHKDbn68x7i9LFgmvf9tMRwvNxzIVQJUpS6_AHya1Er |
| CitedBy_id | crossref_primary_10_1080_00949655_2023_2232504 crossref_primary_10_1016_j_apm_2023_06_039 crossref_primary_10_1007_s10182_021_00403_x crossref_primary_10_1080_03610918_2025_2496769 crossref_primary_10_1109_TNNLS_2022_3189069 crossref_primary_10_1016_j_apm_2021_12_016 |
| ContentType | Journal Article |
| Copyright | Copyright Elsevier BV Nov 2020 |
| Copyright_xml | – notice: Copyright Elsevier BV Nov 2020 |
| DBID | 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1016/j.apm.2020.05.012 |
| DatabaseName | Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Computer and Information Systems Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Psychology Mathematics |
| EISSN | 0307-904X |
| GroupedDBID | -W8 -~X .7I .GO .QK 0BK 0R~ 23M 2DF 4.4 53G 5GY 6J9 7SC 8FD 8VB AAGDL AAGZJ AAHIA AAHSB AAMFJ AAMIU AAPUL AATTQ AAZMC ABCCY ABDBF ABFIM ABIVO ABJNI ABLIJ ABPEM ABRYG ABTAI ABXUL ABXYU ABZLS ACGFS ACGOD ACHQT ACTIO ACTOA ACUHS ADAHI ADCVX ADKVQ ADYSH AECIN AEFOU AEGXH AEISY AEKEX AEMOZ AEMXT AEOZL AEPSL AEYOC AEZRU AFHDM AFRVT AGDLA AGMYJ AGRBW AHDZW AHQJS AIJEM AIYEW AJWEG AKBVH AKVCP ALMA_UNASSIGNED_HOLDINGS ALQZU AVBZW AWYRJ BEJHT BLEHA BMOTO BOHLJ CCCUG CQ1 CS3 DGFLZ DKSSO EAP EBR EBS EBU EDJ EMK EPL EPS EST ESX E~B E~C F5P FEDTE G-F GTTXZ H13 HF~ HVGLF HZ~ IPNFZ J.O JQ2 K1G KYCEM L7M LJTGL L~C L~D M4Z NA5 O9- P2P PQQKQ QWB RIG RNANH ROSJB RSYQP S-F STATR TASJS TBQAZ TDBHL TEH TFH TFL TFW TH9 TNTFI TRJHH TUROJ TUS TWZ UPT UT5 UT9 VAE ZL0 ~01 ~S~ |
| ID | FETCH-LOGICAL-c325t-f06ac824ab276edbd4dba84ed7d1c89e8a4102e2f51f4beb9f7f0cdb15745b4c3 |
| ISICitedReferencesCount | 7 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000557208100030&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1088-8691 |
| IngestDate | Fri Jul 25 23:53:26 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c325t-f06ac824ab276edbd4dba84ed7d1c89e8a4102e2f51f4beb9f7f0cdb15745b4c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2447305438 |
| PQPubID | 2045280 |
| ParticipantIDs | proquest_journals_2447305438 |
| PublicationCentury | 2000 |
| PublicationDate | 20201101 |
| PublicationDateYYYYMMDD | 2020-11-01 |
| PublicationDate_xml | – month: 11 year: 2020 text: 20201101 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Applied Mathematical Modelling |
| PublicationYear | 2020 |
| Publisher | Elsevier BV |
| Publisher_xml | – name: Elsevier BV |
| SSID | ssj0012860 ssj0005904 |
| Score | 2.329042 |
| Snippet | Heavy-tailed noise or strongly correlated predictors often go with the multivariate linear regression model. To tackle with these problems, this paper focuses... |
| SourceID | proquest |
| SourceType | Aggregation Database |
| StartPage | 571 |
| SubjectTerms | Algorithms Data analysis Multivariate analysis Noise prediction Outliers (statistics) Regression analysis Regression models Robustness (mathematics) |
| Title | Low-rank elastic-net regularized multivariate Huber regression model |
| URI | https://www.proquest.com/docview/2447305438 |
| Volume | 87 |
| WOSCitedRecordID | wos000557208100030&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: PRVAWR databaseName: Taylor & Francis Online Journals customDbUrl: eissn: 0307-904X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0012860 issn: 1088-8691 databaseCode: TFW dateStart: 19970301 isFulltext: true titleUrlDefault: https://www.tandfonline.com providerName: Taylor & Francis |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEF5B4dAeEBRQC6XyAXGJtrI3a-_ukUejHELg4NKol8j7sJqqclInKW1_PbMPx6aVEBy4WMluZDmZLzPf7szsh9D7uDRai0xiCJUUU82skLtKMEt0yQ2xeUfXKDxi4zGfTMT3UG67dHICrKr4zY1Y_FdTwxgY27bO_oO5NzeFAXgNRocrmB2uf2X40fwntkrsPQPEGGZxZVa92knO17M74JeuhvAa3gDN7A3X0tR22tfDVl4ap0tZG576dXPAq23ntZ-6bMKeKw_w_usTjN2dt_1lZ0Hv-tRUF-sWicN12Kc-O5_dzsNtwuYDrDST3zYf2q6YHx0fCo4L88yLcB2Z0JwVMyxiX4zZON4Qab3nTL0SywOP7jcXLo6KhT03gMT-nFXShq8mZT_-Nh2cjEbT_HiSf1hcYSssZhPwQWXlMXpCWCps1V8-OG2rgIRdjoasE-G-q7z5Ak0W3NUD3nuAB7HbEZL8OXoWVhLRR4-AF-iRqXbRTmul5S7a3oS325foSwOMqAOMqAOMqAuMyAEjaoEROWC8QieD4_zzEAcNDaz6JF3hMs4KxQktJGGZ0VJTLQtOjWY6UVwYXlCgmIaUaVJSaaQoWRkrLZOU0VRS1X-Ntqp5ZfZQlGRAplUmClvDy42SwJ4JK4FRm6xIiN5HB80vMg3_h-UU2CPEkJT2-Zs_T79F2y3ADtDWql6bd-ipul7NlvWhM9ov1Q1cCw |
| linkProvider | Taylor & Francis |
| 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=Low-rank+elastic-net+regularized+multivariate+Huber+regression+model&rft.jtitle=Applied+Mathematical+Modelling&rft.au=Chen%2C+Bingzhen&rft.au=Zhai%2C+Wenjuan&rft.au=Huang%2C+Zhiyong&rft.date=2020-11-01&rft.pub=Elsevier+BV&rft.issn=1088-8691&rft.eissn=0307-904X&rft.volume=87&rft.spage=571&rft_id=info:doi/10.1016%2Fj.apm.2020.05.012&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1088-8691&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1088-8691&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1088-8691&client=summon |