Harmonization Shared Autoencoder Gaussian Process Latent Variable Model With Relaxed Hamming Distance
Multiview learning has shown its superiority in visual classification compared with the single-view-based methods. Especially, due to the powerful representation capacity, the Gaussian process latent variable model (GPLVM)-based multiview approaches have achieved outstanding performances. However, m...
Uloženo v:
| Vydáno v: | IEEE transaction on neural networks and learning systems Ročník 32; číslo 11; s. 5093 - 5107 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Piscataway
IEEE
01.11.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 2162-237X, 2162-2388, 2162-2388 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Multiview learning has shown its superiority in visual classification compared with the single-view-based methods. Especially, due to the powerful representation capacity, the Gaussian process latent variable model (GPLVM)-based multiview approaches have achieved outstanding performances. However, most of them only follow the assumption that the shared latent variables can be generated from or projected to the multiple observations but fail to exploit the harmonization in the back constraint and adaptively learn a classifier according to these learned variables, which would result in performance degradation. To tackle these two issues, in this article, we propose a novel harmonization shared autoencoder GPLVM with a relaxed Hamming distance (HSAGP-RHD). Particularly, an autoencoder structure with the Gaussian process (GP) prior is first constructed to learn the shared latent variable for multiple views. To enforce the agreement among various views in the encoder, a harmonization constraint is embedded into the model by making consistency for the view-specific similarity. Furthermore, we also propose a novel discriminative prior, which is directly imposed on the latent variable to simultaneously learn the fused features and adaptive classifier in a unit model. In detail, the centroid matrix corresponding to the centroids of different categories is first obtained. A relaxed Hamming distance (RHD)-based measurement is subsequently presented to measure the similarity and dissimilarity between the latent variable and centroids, not only allowing us to get the closed-form solutions but also encouraging the points belonging to the same class to be close, while those belonging to different classes to be far. Due to this novel prior, the category of the out-of-sample is also allowed to be simply assigned in the testing phase. Experimental results conducted on three real-world data sets demonstrate the effectiveness of the proposed method compared with state-of-the-art approaches. |
|---|---|
| AbstractList | Multiview learning has shown its superiority in visual classification compared with the single-view-based methods. Especially, due to the powerful representation capacity, the Gaussian process latent variable model (GPLVM)-based multiview approaches have achieved outstanding performances. However, most of them only follow the assumption that the shared latent variables can be generated from or projected to the multiple observations but fail to exploit the harmonization in the back constraint and adaptively learn a classifier according to these learned variables, which would result in performance degradation. To tackle these two issues, in this article, we propose a novel harmonization shared autoencoder GPLVM with a relaxed Hamming distance (HSAGP-RHD). Particularly, an autoencoder structure with the Gaussian process (GP) prior is first constructed to learn the shared latent variable for multiple views. To enforce the agreement among various views in the encoder, a harmonization constraint is embedded into the model by making consistency for the view-specific similarity. Furthermore, we also propose a novel discriminative prior, which is directly imposed on the latent variable to simultaneously learn the fused features and adaptive classifier in a unit model. In detail, the centroid matrix corresponding to the centroids of different categories is first obtained. A relaxed Hamming distance (RHD)-based measurement is subsequently presented to measure the similarity and dissimilarity between the latent variable and centroids, not only allowing us to get the closed-form solutions but also encouraging the points belonging to the same class to be close, while those belonging to different classes to be far. Due to this novel prior, the category of the out-of-sample is also allowed to be simply assigned in the testing phase. Experimental results conducted on three real-world data sets demonstrate the effectiveness of the proposed method compared with state-of-the-art approaches. Multiview learning has shown its superiority in visual classification compared with the single-view-based methods. Especially, due to the powerful representation capacity, the Gaussian process latent variable model (GPLVM)-based multiview approaches have achieved outstanding performances. However, most of them only follow the assumption that the shared latent variables can be generated from or projected to the multiple observations but fail to exploit the harmonization in the back constraint and adaptively learn a classifier according to these learned variables, which would result in performance degradation. To tackle these two issues, in this article, we propose a novel harmonization shared autoencoder GPLVM with a relaxed Hamming distance (HSAGP-RHD). Particularly, an autoencoder structure with the Gaussian process (GP) prior is first constructed to learn the shared latent variable for multiple views. To enforce the agreement among various views in the encoder, a harmonization constraint is embedded into the model by making consistency for the view-specific similarity. Furthermore, we also propose a novel discriminative prior, which is directly imposed on the latent variable to simultaneously learn the fused features and adaptive classifier in a unit model. In detail, the centroid matrix corresponding to the centroids of different categories is first obtained. A relaxed Hamming distance (RHD)-based measurement is subsequently presented to measure the similarity and dissimilarity between the latent variable and centroids, not only allowing us to get the closed-form solutions but also encouraging the points belonging to the same class to be close, while those belonging to different classes to be far. Due to this novel prior, the category of the out-of-sample is also allowed to be simply assigned in the testing phase. Experimental results conducted on three real-world data sets demonstrate the effectiveness of the proposed method compared with state-of-the-art approaches.Multiview learning has shown its superiority in visual classification compared with the single-view-based methods. Especially, due to the powerful representation capacity, the Gaussian process latent variable model (GPLVM)-based multiview approaches have achieved outstanding performances. However, most of them only follow the assumption that the shared latent variables can be generated from or projected to the multiple observations but fail to exploit the harmonization in the back constraint and adaptively learn a classifier according to these learned variables, which would result in performance degradation. To tackle these two issues, in this article, we propose a novel harmonization shared autoencoder GPLVM with a relaxed Hamming distance (HSAGP-RHD). Particularly, an autoencoder structure with the Gaussian process (GP) prior is first constructed to learn the shared latent variable for multiple views. To enforce the agreement among various views in the encoder, a harmonization constraint is embedded into the model by making consistency for the view-specific similarity. Furthermore, we also propose a novel discriminative prior, which is directly imposed on the latent variable to simultaneously learn the fused features and adaptive classifier in a unit model. In detail, the centroid matrix corresponding to the centroids of different categories is first obtained. A relaxed Hamming distance (RHD)-based measurement is subsequently presented to measure the similarity and dissimilarity between the latent variable and centroids, not only allowing us to get the closed-form solutions but also encouraging the points belonging to the same class to be close, while those belonging to different classes to be far. Due to this novel prior, the category of the out-of-sample is also allowed to be simply assigned in the testing phase. Experimental results conducted on three real-world data sets demonstrate the effectiveness of the proposed method compared with state-of-the-art approaches. |
| Author | Zhang, Bob Xu, Yong Wu, Feng Lu, Guangming Li, Jinxing Zhang, David |
| Author_xml | – sequence: 1 givenname: Jinxing orcidid: 0000-0001-5156-0305 surname: Li fullname: Li, Jinxing email: lijinxing158@gmail.com organization: School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China – sequence: 2 givenname: Bob orcidid: 0000-0001-5628-6237 surname: Zhang fullname: Zhang, Bob email: bobzhang@um.edu.mo organization: Faculty of Science and Technology, University of Macau, Macau, China – sequence: 3 givenname: Guangming orcidid: 0000-0003-1578-2634 surname: Lu fullname: Lu, Guangming email: luguangm@hit.edu.cn organization: Harbin Institute of Technology, Shenzhen, China – sequence: 4 givenname: Yong orcidid: 0000-0003-0530-2123 surname: Xu fullname: Xu, Yong email: yongxu@ymail.com organization: Harbin Institute of Technology, Shenzhen, China – sequence: 5 givenname: Feng surname: Wu fullname: Wu, Feng email: fengwu@ustc.edu.cn organization: University of Science and Technology of China, Hefei, China – sequence: 6 givenname: David orcidid: 0000-0002-5027-5286 surname: Zhang fullname: Zhang, David email: davidzhang@cuhk.edu.cn organization: School of Data Science, The Chinese University of Hong Kong, Shenzhen, China |
| BookMark | eNp9kU1PFTEUhhsDEUT-gG6auHFzL22n048lAeWaXNAIfuwm53bOSMlMi20nUX-9hUtYsKCb0ybPc05z3ldkJ8SAhLzhbMk5s0dXFxfry6Vggi0bJpTR6gXZF1yJhWiM2Xm865975DDnG1aPYq2S9iXZa6qiGTP7BFeQphj8Pyg-Bnp5DQl7ejyXiMHFHhM9gzlnD4F-SdFhznQNBUOh3yF52IxIzys20h--XNOvOMKf6q9gmnz4RU99LhAcvia7A4wZDx_qAfn28cPVyWqx_nz26eR4vXCNMGUBba-YkIoPlim9AQnWOGiQa6MlSLSqR843tr5644weGLMah7blZqj4pjkg77d9b1P8PWMu3eSzw3GEgHHOnZDSilYIbSv67gl6E-cU6u860RrVWlZXWimxpVyKOSccutvkJ0h_O866uxy6-xy6uxy6hxyqZJ5Izpf7_ZYEfnxefbtVPSI-zrI1S2ll8x9wVJXI |
| CODEN | ITNNAL |
| CitedBy_id | crossref_primary_10_1109_ACCESS_2024_3437328 crossref_primary_10_1016_j_compbiomed_2023_107837 |
| Cites_doi | 10.1109/TCYB.2019.2904052 10.1109/ICCV.2015.461 10.1109/ICDM.2014.29 10.1109/TSMCB.2010.2057422 10.1109/TSMCB.2012.2196995 10.1109/CVPR.2017.348 10.1109/TIP.2018.2865885 10.1109/TPAMI.2013.140 10.1109/CVPR.2009.5206594 10.1145/1646396.1646452 10.1109/TIP.2014.2375634 10.1109/TCYB.2017.2742705 10.1109/TPAMI.2018.2847335 10.1016/j.ins.2016.09.031 10.1109/TIP.2017.2713045 10.1145/1273496.1273613 10.1162/0899766042321814 10.1109/CVPR.2016.524 10.1016/j.inffus.2018.02.005 10.1109/TPAMI.2015.2435740 10.1109/TCYB.2015.2502248 10.1007/978-3-030-01258-8_44 10.1109/TCSVT.2016.2607842 10.1016/j.eswa.2017.07.023 10.1109/TPAMI.2018.2877660 10.1109/CVPR.2017.461 10.1109/TNNLS.2017.2761401 10.1145/3240508.3240520 10.1016/j.cviu.2009.03.008 10.1109/TPAMI.2019.2942028 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| DBID | 97E RIA RIE AAYXX CITATION 7QF 7QO 7QP 7QQ 7QR 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8 |
| DOI | 10.1109/TNNLS.2020.3026876 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE/IET Electronic Library CrossRef Aluminium Industry Abstracts Biotechnology Research Abstracts Calcium & Calcified Tissue Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Neurosciences Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef Materials Research Database Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Materials Business File Aerospace Database Engineered Materials Abstracts Biotechnology Research Abstracts Chemoreception Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Civil Engineering Abstracts Aluminium Industry Abstracts Electronics & Communications Abstracts Ceramic Abstracts Neurosciences Abstracts METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Solid State and Superconductivity Abstracts Engineering Research Database Calcium & Calcified Tissue Abstracts Corrosion Abstracts MEDLINE - Academic |
| DatabaseTitleList | Materials Research Database MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 2162-2388 |
| EndPage | 5107 |
| ExternalDocumentID | 10_1109_TNNLS_2020_3026876 9216494 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: China Postdoctoral Science Foundation grantid: 2019TQ0316; 2019M662198; 2020TQ0319 funderid: 10.13039/501100002858 – fundername: Shenzhen Research Institute of Big Data funderid: 10.13039/501100020785 – fundername: NSFC grantid: 61906162 funderid: 10.13039/501100001809 – fundername: Shenzhen Institute of Artificial Intelligence and Robotics for Society (Open Project) |
| GroupedDBID | 0R~ 4.4 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK ACPRK AENEX AFRAH AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD IFIPE IPLJI JAVBF M43 MS~ O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION 7QF 7QO 7QP 7QQ 7QR 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8 |
| ID | FETCH-LOGICAL-c328t-a5d602461f9067ba4a98ca3e17874a4e96de11b9874d8c87f0097ef5518f7bab3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000711638200030&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2162-237X 2162-2388 |
| IngestDate | Thu Oct 02 07:17:18 EDT 2025 Sun Nov 30 04:10:59 EST 2025 Sat Nov 29 01:40:08 EST 2025 Tue Nov 18 22:18:26 EST 2025 Wed Aug 27 05:08:54 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 11 |
| 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-c328t-a5d602461f9067ba4a98ca3e17874a4e96de11b9874d8c87f0097ef5518f7bab3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-0530-2123 0000-0002-5027-5286 0000-0001-5156-0305 0000-0001-5628-6237 0000-0003-1578-2634 |
| PMID | 33027008 |
| PQID | 2586590876 |
| PQPubID | 85436 |
| PageCount | 15 |
| ParticipantIDs | crossref_primary_10_1109_TNNLS_2020_3026876 ieee_primary_9216494 crossref_citationtrail_10_1109_TNNLS_2020_3026876 proquest_miscellaneous_2449252279 proquest_journals_2586590876 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-11-01 |
| PublicationDateYYYYMMDD | 2021-11-01 |
| PublicationDate_xml | – month: 11 year: 2021 text: 2021-11-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE transaction on neural networks and learning systems |
| PublicationTitleAbbrev | TNNLS |
| PublicationYear | 2021 |
| 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 ref15 ref14 ref11 andrew (ref17) 2013 ref16 bach (ref19) 2005 yang (ref32) 2012 zhang (ref52) 2019 gao (ref7) 2011; 41 ref51 wang (ref22) 2015 kemp (ref46) 2006; 3 rasmussen (ref44) 2006 ref45 ref48 ref47 ref41 ref8 ref4 ref3 ref6 ref5 ek (ref12) 2009 simonyan (ref50) 2014 ref35 ref34 ref31 rasmussen (ref36) 2003 ref30 ref33 williams (ref38) 2006; 2 archambeau (ref18) 2009 ref2 ref39 bertsekas (ref42) 2014 krizhevsky (ref49) 2012 ref24 lawrence (ref10) 2004; 16 ref23 ref26 ref25 petersen (ref43) 2008 fukumizu (ref20) 2007; 8 ref28 ref27 ref29 lawrence (ref9) 2005; 6 li (ref1) 2018 rasmussen (ref37) 2010; 11 andrew (ref21) 2013 norouzi (ref40) 2012 |
| References_xml | – ident: ref35 doi: 10.1109/TCYB.2019.2904052 – start-page: 1097 year: 2012 ident: ref49 article-title: Imagenet classification with deep convolutional neural networks publication-title: Proc Adv Neural Inf Process Syst – ident: ref5 doi: 10.1109/ICCV.2015.461 – year: 2008 ident: ref43 publication-title: The Matrix Cookbook – ident: ref28 doi: 10.1109/ICDM.2014.29 – volume: 41 start-page: 425 year: 2011 ident: ref7 article-title: Supervised Gaussian process latent variable model for dimensionality reduction publication-title: IEEE Trans Syst Man Cybern B (Cybern ) doi: 10.1109/TSMCB.2010.2057422 – volume: 11 start-page: 3011 year: 2010 ident: ref37 article-title: Gaussian processes for machine learning (GPML) toolbox publication-title: J Mach Learn Res – volume: 16 start-page: 329 year: 2004 ident: ref10 article-title: Gaussian process latent variable models for visualisation of high dimensional data publication-title: Proc Adv Neural Inf Process Syst – ident: ref8 doi: 10.1109/TSMCB.2012.2196995 – ident: ref41 doi: 10.1109/CVPR.2017.348 – start-page: 73 year: 2009 ident: ref18 article-title: Sparse probabilistic projections publication-title: Proc Adv Neural Inf Process Syst – ident: ref31 doi: 10.1109/TIP.2018.2865885 – ident: ref48 doi: 10.1109/TPAMI.2013.140 – ident: ref47 doi: 10.1109/CVPR.2009.5206594 – ident: ref51 doi: 10.1145/1646396.1646452 – ident: ref15 doi: 10.1109/TIP.2014.2375634 – ident: ref25 doi: 10.1109/TCYB.2017.2742705 – start-page: 1061 year: 2012 ident: ref40 article-title: Hamming distance metric learning publication-title: Proc Adv Neural Inf Process Syst – ident: ref6 doi: 10.1109/TPAMI.2018.2847335 – volume: 8 start-page: 361 year: 2007 ident: ref20 article-title: Statistical consistency of kernel canonical correlation analysis publication-title: J Mach Learn Res – ident: ref3 doi: 10.1016/j.ins.2016.09.031 – ident: ref34 doi: 10.1109/TIP.2017.2713045 – start-page: 557 year: 2019 ident: ref52 article-title: CPM-Nets: Cross partial multi-view networks publication-title: Proc Adv Neural Inf Process Syst – start-page: 1247 year: 2013 ident: ref21 article-title: Deep canonical correlation analysis publication-title: Proc Int Conf Mach Learn – start-page: 63 year: 2003 ident: ref36 article-title: Gaussian processes in machine learning publication-title: Machine Learning Summer School – start-page: 3498 year: 2018 ident: ref1 article-title: A probabilistic hierarchical model for multi-view and multi-feature classification publication-title: Proc 32nd AAAI Conf Artif Intell – ident: ref14 doi: 10.1145/1273496.1273613 – ident: ref16 doi: 10.1162/0899766042321814 – ident: ref24 doi: 10.1109/CVPR.2016.524 – ident: ref2 doi: 10.1016/j.inffus.2018.02.005 – volume: 6 start-page: 1783 year: 2005 ident: ref9 article-title: Probabilistic non-linear principal component analysis with Gaussian process latent variable models publication-title: J Mach Learn Res – year: 2014 ident: ref50 article-title: Very deep convolutional networks for large-scale image recognition publication-title: arXiv 1409 1556 – volume: 2 year: 2006 ident: ref38 publication-title: Gaussian Processes for Machine Learning – ident: ref23 doi: 10.1109/TPAMI.2015.2435740 – start-page: 1083 year: 2015 ident: ref22 article-title: On deep multi-view representation learning publication-title: Proc Int Conf Mach Learn – ident: ref4 doi: 10.1109/TCYB.2015.2502248 – ident: ref26 doi: 10.1007/978-3-030-01258-8_44 – start-page: 2224 year: 2012 ident: ref32 article-title: Relaxed collaborative representation for pattern classification publication-title: Proc IEEE Conf Comput Vis Pattern Recognit – year: 2009 ident: ref12 article-title: Shared Gaussian process latent variable models – start-page: 1247 year: 2013 ident: ref17 article-title: Deep canonical correlation analysis publication-title: Proc Int Conf Mach Learn – volume: 3 start-page: 5 year: 2006 ident: ref46 article-title: Learning systems of concepts with an infinite relational model publication-title: Proc AAAI – ident: ref27 doi: 10.1109/TCSVT.2016.2607842 – year: 2005 ident: ref19 article-title: A probabilistic interpretation of canonical correlation analysis – ident: ref33 doi: 10.1016/j.eswa.2017.07.023 – year: 2006 ident: ref44 publication-title: Gaussian Processes for Machine Learning – ident: ref30 doi: 10.1109/TPAMI.2018.2877660 – ident: ref29 doi: 10.1109/CVPR.2017.461 – ident: ref11 doi: 10.1109/TNNLS.2017.2761401 – ident: ref13 doi: 10.1145/3240508.3240520 – ident: ref45 doi: 10.1016/j.cviu.2009.03.008 – ident: ref39 doi: 10.1109/TPAMI.2019.2942028 – year: 2014 ident: ref42 publication-title: Constrained Optimization and Lagrange Multiplier Methods |
| SSID | ssj0000605649 |
| Score | 2.382581 |
| Snippet | Multiview learning has shown its superiority in visual classification compared with the single-view-based methods. Especially, due to the powerful... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 5093 |
| SubjectTerms | Centroids Classifiers Coders Constraint modelling Correlation Gaussian process Gaussian process (GP) Gaussian processes Hamming distance harmonization Image classification latent variable multiview Performance degradation Similarity |
| Title | Harmonization Shared Autoencoder Gaussian Process Latent Variable Model With Relaxed Hamming Distance |
| URI | https://ieeexplore.ieee.org/document/9216494 https://www.proquest.com/docview/2586590876 https://www.proquest.com/docview/2449252279 |
| Volume | 32 |
| WOSCitedRecordID | wos000711638200030&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: 2162-2388 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000605649 issn: 2162-237X databaseCode: RIE dateStart: 20120101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fS9xAEB5USvFFbVU8a2ULfdNostn82Edpa32Qo1Cr9xY2uxMUJCd3ifjnO7O3FwSL4FtCZpOQmcnM7O73DcB3g6TpRjeRxMxEKstcVGJqojyVzliUsSucbzZRjMflZKL_rMDxgIVBRL_5DE_40K_lu6ntearsVEtK7rVahdWiyBdYrWE-Jaa8PPfZLgnJSKbFZImRifXp1Xh8-ZeqQUlFKlUd9AtYh48pr9nF3FjyRUjyPVZe_Zh9tDnffN97bsFGyCrF2cIMPsEKtp9hc9mxQQQH3ga8MDOyu4C9FMzWjE6c9d2U-Swdif42_ZxxlSIgCMQlJaNtJ66ppmaUleDmaffi5q67FbyR7onG8-w3RUDxk3NRes4O_Dv_dfXjIgqNFiKbyrKLTObymInlGk3BqzbK6NKaFBPyZmUU6txhktSazlxpy6Jh9Ac2TObWkHid7sJaO21xDwQl64WpEykTVSuXNHWtUVlpY5ObRNfFCJLlt65sYCHnZhj3la9GYl15VVWsqiqoagRHw5iHBQfHm9LbrJFBMihjBAdLlVbBTeeVzMqcm77zqG_DZXIwXjUxLU57klHM38hEi_v_v_MXWJe80cUDFA9grZv1-BU-2Mfubj47JFudlIfeVp8BZnfkLg |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Nb9QwEB2VgmgvFCiILQWMxA1CE8f58LEqlEUsERIL7C1y7ImoVGWr3QTx8zvj9UZIRUjcEmWcRBlPPGP7vQfwyiB5utVtJDEzkcoyF5WYmihPpTMWZewK58UmiqoqFwv9ZQfejFgYRPSbz_AtH_q1fLe0A0-VnWhJyb1Wt-B2ppSMN2itcUYlpsw89_kumclIpsVii5KJ9cm8qmZfqR6UVKZS3UE_gX24m_KqXczSkn8MSl5l5cav2Y835wf_96b34V7IK8XppiM8gB3sHsLBVrNBhBA-BJyaFfW8gL4UzNeMTpwO_ZIZLR2ZfjDDmpGVImAIxIzS0a4X36mqZpyVYPm0S_Hjov8peCvdb2rP8980Bop3nI3Scx7Bt_P387NpFKQWIpvKso9M5vKYqeVaTcNXY5TRpTUpJhTPyijUucMkaTSdudKWRcv4D2yZzq0l8yZ9DLvdssMnIChdL0yTSJmoRrmkbRqNykobm9wkuikmkGy_dW0DDznLYVzWvh6Jde1dVbOr6uCqCbwe21xtWDj-aX3IHhktgzMmcLx1aR0CdV3LrMxZ9p1bvRwvU4jxuonpcDmQjWIGR6ZaPPr7nV_A3nT-eVbPPlafnsK-5G0vHq54DLv9asBncMf-6i_Wq-e-x14DCq_mjQ |
| 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=Harmonization+Shared+Autoencoder+Gaussian+Process+Latent+Variable+Model+With+Relaxed+Hamming+Distance&rft.jtitle=IEEE+transaction+on+neural+networks+and+learning+systems&rft.au=Li%2C+Jinxing&rft.au=Zhang%2C+Bob&rft.au=Lu%2C+Guangming&rft.au=Xu%2C+Yong&rft.date=2021-11-01&rft.issn=2162-237X&rft.eissn=2162-2388&rft.volume=32&rft.issue=11&rft.spage=5093&rft.epage=5107&rft_id=info:doi/10.1109%2FTNNLS.2020.3026876&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TNNLS_2020_3026876 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2162-237X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2162-237X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2162-237X&client=summon |