One-Class Classification Using ℓp-Norm Multiple Kernel Fisher Null Approach

We address the one-class classification (OCC) problem and advocate a one-class MKL (multiple kernel learning) approach for this purpose. To this aim, based on the Fisher null-space OCC principle, we present a multiple kernel learning algorithm where an ℓ p -norm regularisation ( p ≥ 1) is considered...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on image processing Ročník 32; s. 1
Hlavný autor:	Arashloo, Shervin Rahimzadeh
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Classification Kernel Kernels Machine learning multiple kernel learning One-class classification one-class Fisher null transformation Optimization Pattern recognition Regularization Saddle points Search problems Support vector machines Task analysis Training ℓ<sub xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> p -norm regularisation
ISSN:	1057-7149, 1941-0042, 1941-0042
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	We address the one-class classification (OCC) problem and advocate a one-class MKL (multiple kernel learning) approach for this purpose. To this aim, based on the Fisher null-space OCC principle, we present a multiple kernel learning algorithm where an ℓ p -norm regularisation ( p ≥ 1) is considered for kernel weight learning. We cast the proposed one-class MKL problem as a min-max saddle point Lagrangian optimisation task and propose an efficient approach to optimise it. An extension of the proposed approach is also considered where several related one-class MKL tasks are learned concurrently by constraining them to share common weights for kernels. An extensive evaluation of the proposed MKL approach on a range of data sets from different application domains confirms its merits against the baseline and several other algorithms.
AbstractList	We address the one-class classification (OCC) problem and advocate a one-class MKL (multiple kernel learning) approach for this purpose. To this aim, based on the Fisher null-space OCC principle, we present a multiple kernel learning algorithm where an [Formula Omitted]-norm regularisation ([Formula Omitted]) is considered for kernel weight learning. We cast the proposed one-class MKL problem as a min-max saddle point Lagrangian optimisation task and propose an efficient approach to optimise it. An extension of the proposed approach is also considered where several related one-class MKL tasks are learned concurrently by constraining them to share common weights for kernels. An extensive evaluation of the proposed MKL approach on a range of data sets from different application domains confirms its merits against the baseline and several other algorithms. We address the one-class classification (OCC) problem and advocate a one-class MKL (multiple kernel learning) approach for this purpose. To this aim, based on the Fisher null-space OCC principle, we present a multiple kernel learning algorithm where an ℓ p -norm regularisation ( p ≥ 1) is considered for kernel weight learning. We cast the proposed one-class MKL problem as a min-max saddle point Lagrangian optimisation task and propose an efficient approach to optimise it. An extension of the proposed approach is also considered where several related one-class MKL tasks are learned concurrently by constraining them to share common weights for kernels. An extensive evaluation of the proposed MKL approach on a range of data sets from different application domains confirms its merits against the baseline and several other algorithms. We address the one-class classification (OCC) problem and advocate a one-class MKL (multiple kernel learning) approach for this purpose. To this aim, based on the Fisher null-space OCC principle, we present a multiple kernel learning algorithm where an $\ell _{p}$ -norm regularisation ( $p \geq 1$ ) is considered for kernel weight learning. We cast the proposed one-class MKL problem as a min-max saddle point Lagrangian optimisation task and propose an efficient approach to optimise it. An extension of the proposed approach is also considered where several related one-class MKL tasks are learned concurrently by constraining them to share common weights for kernels. An extensive evaluation of the proposed MKL approach on a range of data sets from different application domains confirms its merits against the baseline and several other algorithms.We address the one-class classification (OCC) problem and advocate a one-class MKL (multiple kernel learning) approach for this purpose. To this aim, based on the Fisher null-space OCC principle, we present a multiple kernel learning algorithm where an $\ell _{p}$ -norm regularisation ( $p \geq 1$ ) is considered for kernel weight learning. We cast the proposed one-class MKL problem as a min-max saddle point Lagrangian optimisation task and propose an efficient approach to optimise it. An extension of the proposed approach is also considered where several related one-class MKL tasks are learned concurrently by constraining them to share common weights for kernels. An extensive evaluation of the proposed MKL approach on a range of data sets from different application domains confirms its merits against the baseline and several other algorithms. We address the one-class classification (OCC) problem and advocate a one-class MKL (multiple kernel learning) approach for this purpose. To this aim, based on the Fisher null-space OCC principle, we present a multiple kernel learning algorithm where an $\ell _{p}$ -norm regularisation ( $p \geq 1$ ) is considered for kernel weight learning. We cast the proposed one-class MKL problem as a min-max saddle point Lagrangian optimisation task and propose an efficient approach to optimise it. An extension of the proposed approach is also considered where several related one-class MKL tasks are learned concurrently by constraining them to share common weights for kernels. An extensive evaluation of the proposed MKL approach on a range of data sets from different application domains confirms its merits against the baseline and several other algorithms.
Author	Arashloo, Shervin Rahimzadeh
Author_xml	– sequence: 1 givenname: Shervin Rahimzadeh orcidid: 0000-0003-0189-4774 surname: Arashloo fullname: Arashloo, Shervin Rahimzadeh organization: Department of Computer Engineering, Faculty of Engineering, Bilkent University, Ankara, Turkey
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/37028349$$D View this record in MEDLINE/PubMed
BookMark	eNp9kb9u2zAQxokgRZyk2TMEhYAuXeQe_1nkGBh1a9RJOsQzQUmnmAEtqaQ0ZO8b9A3zJGFsBygydDkeiN939-G-M3Lcdi0ScklhSinor_fLX1MGjE85k5ICOyKnVAuaAwh2nHqQRV5QoSfkLMZHACoknZ2QCS-AKS70Kbm5azGfextjtquucZUdXNdm6-jah-z5z98-v-3CNrsZ_eB6j9lPDC36bOHiBkN2O3qfXfd96Gy1-Ug-NNZHvDi852S9-HY__5Gv7r4v59ervOJCDXkpmoZLpBWFpq513aga0DYlsLoslRUWWWGVYMk_KGHTl2ICdS2lpVLNan5OvuznprW_R4yD2bpYofe2xW6MhhVaFaA45Qn9_A597MbQJneJUlooqhUk6tOBGsst1qYPbmvDk3k7VAJme6AKXYwBG1O5YXeoIVjnDQXzmohJiZjXRMwhkSSEd8K32f-RXO0lDhH_waEAmby-AFGGlWI
CODEN	IIPRE4
CitedBy_id	crossref_primary_10_1007_s10462_023_10673_3 crossref_primary_10_3390_math13071050
Cites_doi	10.1109/TNNLS.2016.2635151 10.1109/TCYB.2021.3052357 10.1109/CVPR.2017.243 10.1109/ICCVW.2015.114 10.1109/CVPR.2013.107 10.1109/TIFS.2014.2359587 10.1109/TIFS.2019.2922241 10.1016/j.patcog.2020.107696 10.1109/CVPR.2016.90 10.1109/JBHI.2013.2274479 10.1109/TNNLS.2014.2334137 10.1109/TCYB.2018.2804940 10.1145/1541880.1541882 10.1007/978-1-4615-2025-2_7 10.1109/CVPR.2015.7298594 10.1109/TSMCC.2008.2007248 10.1162/089976601750264965 10.1109/TPAMI.2013.149 10.1016/j.patcog.2006.07.009 10.1137/1.9780898718058 10.1109/WACV48630.2021.00205 10.1023/B:MACH.0000008084.60811.49 10.1201/9781315274508 10.1007/978-3-540-87536-9_23 10.1145/2980258.2980319 10.1109/ICCIC.2015.7435726 10.1109/CVPR.2019.00301 10.1109/ICVGIP.2008.47 10.1109/CVPR.2018.00356 10.1109/TKDE.2008.239 10.1109/ICCV48922.2021.00867 10.1109/CVPR.2018.00907 10.1109/TCSVT.2020.3046505 10.1016/j.patcog.2013.06.005 10.1109/TPAMI.2009.98 10.1007/s11263-009-0275-4 10.1109/GlobalSIP.2015.7418328 10.1109/TPAMI.2019.2895608 10.1109/LSP.2018.2889273 10.1109/TIFS.2008.2008216 10.1109/ACCESS.2017.2729161 10.1109/CVPR.2013.433 10.1109/ICPR.2018.8546301 10.1109/CVPR.2006.100 10.1109/CVPR46437.2021.01466 10.1016/j.neucom.2020.08.068 10.1016/j.patcog.2012.09.002 10.1109/CVPR.2018.00474 10.1109/TII.2014.2330796 10.1109/CVPR.2017.460 10.1109/TNNLS.2020.2979823 10.1109/CVPR42600.2020.01419 10.1109/FG.2017.77 10.1109/TNNLS.2017.2785329 10.5555/3294996.3295163 10.1109/TPAMI.2013.212 10.1109/CVPR.2019.00057 10.1109/TCYB.2018.2838668 10.1002/cpe.2933 10.1609/aaai.v33i01.33013991 10.1109/TIFS.2020.2985530 10.1109/TPAMI.2010.183 10.1109/TIP.2019.2917862 10.1109/CVPR.2017.439 10.1017/S026988891300043X
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
DBID	97E RIA RIE AAYXX CITATION NPM 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8
DOI	10.1109/TIP.2023.3255102
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed Computer and Information Systems Abstracts Electronics & Communications 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 MEDLINE - Academic
DatabaseTitle	CrossRef PubMed Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional MEDLINE - Academic
DatabaseTitleList	Technology Research Database MEDLINE - Academic PubMed
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Applied Sciences Engineering
EISSN	1941-0042
EndPage	1
ExternalDocumentID	37028349 10_1109_TIP_2023_3255102 10070580
Genre	orig-research Journal Article
GrantInformation_xml	– fundername: The Scientific and Technological Research Council of Turkey (T?B?TAK) grantid: 121E465
GroupedDBID	--- -~X .DC 0R~ 29I 4.4 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS F5P HZ~ IFIPE IPLJI JAVBF LAI M43 MS~ O9- OCL P2P RIA RIE RNS TAE TN5 53G 5VS AAYXX ABFSI AETIX AGSQL AI. AIBXA ALLEH CITATION E.L EJD H~9 ICLAB IFJZH VH1 AAYOK NPM RIG 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8
ID	FETCH-LOGICAL-c348t-b4ff35e1c10fdd9df8d0eafb02dbb8a4ae27a842105084ab8a824e9d55a1586d3
IEDL.DBID	RIE
ISICitedReferencesCount	2
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000957597000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1057-7149 1941-0042
IngestDate	Sun Sep 28 11:50:46 EDT 2025 Mon Jun 30 10:11:07 EDT 2025 Sun Apr 06 01:21:17 EDT 2025 Tue Nov 18 21:18:39 EST 2025 Sat Nov 29 03:34:41 EST 2025 Wed Aug 27 02:25:56 EDT 2025
IsPeerReviewed	true
IsScholarly	true
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-c348t-b4ff35e1c10fdd9df8d0eafb02dbb8a4ae27a842105084ab8a824e9d55a1586d3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0003-0189-4774
PMID	37028349
PQID	2789481980
PQPubID	85429
PageCount	1
ParticipantIDs	proquest_journals_2789481980 pubmed_primary_37028349 crossref_primary_10_1109_TIP_2023_3255102 proquest_miscellaneous_2798708313 crossref_citationtrail_10_1109_TIP_2023_3255102 ieee_primary_10070580
PublicationCentury	2000
PublicationDate	2023-01-01
PublicationDateYYYYMMDD	2023-01-01
PublicationDate_xml	– month: 01 year: 2023 text: 2023-01-01 day: 01
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: New York
PublicationTitle	IEEE transactions on image processing
PublicationTitleAbbrev	TIP
PublicationTitleAlternate	IEEE Trans Image Process
PublicationYear	2023
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	ref12 ref56 (ref82) 2017 ref15 ref14 ref58 ref53 ref52 ref11 ref55 ref10 ref54 ref17 ref16 ref19 Perera (ref63) 2018 ref18 Kloft (ref33) 2011; 12 Chalapathy (ref57) 2018 Lanckriet (ref36) 2004; 5 Sonnenburg (ref39) 2006; 7 Kozerawski (ref79) 2021 ref92 ref91 ref90 Loosli (ref38) ref46 Ruff (ref77); 80 ref45 ref48 ref47 Stewart (ref89) 2001; 1 ref42 ref86 ref41 ref44 ref87 Krizhevsky (ref61) 2009 ref49 Tax (ref13) 2001 ref8 Yan (ref32) 2012; 13 ref7 ref9 ref4 ref3 ref6 ref5 ref81 ref40 ref84 ref83 Hu (ref74); 33 Ren (ref62) 2018 ref80 ref35 von Neumann (ref43) Ravi (ref65) ref31 ref75 ref30 ref76 Grant (ref88) 2014 ref2 ref1 Cortes (ref34) Griffin (ref59) 2007 ref71 ref70 ref72 Krizhevsky (ref51); 25 ref24 ref68 ref67 ref26 ref25 ref69 ref20 ref64 ref22 Gönen (ref23) 2011; 12 ref66 ref21 Rakotomamonjy (ref37) 2008; 9 ref28 ref27 ref29 Golan (ref78); 31 Simonyan (ref50) 2014 Kliger (ref73) 2018 Feng (ref85) 2020 LeCun (ref60) 2010
References_xml	– ident: ref25 doi: 10.1109/TNNLS.2016.2635151 – ident: ref30 doi: 10.1109/TCYB.2021.3052357 – ident: ref54 doi: 10.1109/CVPR.2017.243 – ident: ref70 doi: 10.1109/ICCVW.2015.114 – volume-title: Information Technology—Biometric Presentation Attack Detection—Part 3: Testing and Reporting year: 2017 ident: ref82 – ident: ref46 doi: 10.1109/CVPR.2013.107 – ident: ref90 doi: 10.1109/TIFS.2014.2359587 – ident: ref86 doi: 10.1109/TIFS.2019.2922241 – ident: ref81 doi: 10.1016/j.patcog.2020.107696 – ident: ref49 doi: 10.1109/CVPR.2016.90 – ident: ref5 doi: 10.1109/JBHI.2013.2274479 – volume: 9 start-page: 2491 year: 2008 ident: ref37 article-title: SimpleMKL publication-title: J. Mach. Learn. Res. – volume: 12 start-page: 2211 issue: 64 year: 2011 ident: ref23 article-title: Multiple kernel learning algorithms publication-title: J. Mach. Learn. Res. – volume: 12 start-page: 953 year: 2011 ident: ref33 article-title: Lp-norm multiple kernel learning publication-title: J. Mach. Lear. Res. – ident: ref24 doi: 10.1109/TNNLS.2014.2334137 – year: 2018 ident: ref57 article-title: Anomaly detection using one-class neural networks publication-title: arXiv:1802.06360 – ident: ref11 doi: 10.1109/TCYB.2018.2804940 – ident: ref1 doi: 10.1145/1541880.1541882 – volume: 5 start-page: 27 year: 2004 ident: ref36 article-title: Learning the kernel matrix with semidefinite programming publication-title: J. Mach. Learn. Res. – ident: ref42 doi: 10.1007/978-1-4615-2025-2_7 – ident: ref48 doi: 10.1109/CVPR.2015.7298594 – ident: ref8 doi: 10.1109/TSMCC.2008.2007248 – ident: ref16 doi: 10.1162/089976601750264965 – ident: ref27 doi: 10.1109/TPAMI.2013.149 – ident: ref18 doi: 10.1016/j.patcog.2006.07.009 – volume: 1 volume-title: Matrix Algorithms: Basic Decompositions year: 2001 ident: ref89 doi: 10.1137/1.9780898718058 – start-page: 1 volume-title: Proc. 25th Conf. Uncertainty Artif. Intell. (UAI) ident: ref34 article-title: L2 regularization for learning kernels – year: 2018 ident: ref63 article-title: Learning deep features for one-class classification publication-title: arXiv:1801.05365 – ident: ref71 doi: 10.1109/WACV48630.2021.00205 – ident: ref14 doi: 10.1023/B:MACH.0000008084.60811.49 – ident: ref44 doi: 10.1201/9781315274508 – volume: 13 start-page: 607 issue: 1 year: 2012 ident: ref32 article-title: Non-sparse multiple kernel Fisher discriminant analysis publication-title: J. Mach. Learn. Res. – start-page: 1 volume-title: Proc. 5th Int. Conf. Learn. Represent. (ICLR) ident: ref65 article-title: Optimization as a model for few-shot learning – ident: ref41 doi: 10.1007/978-3-540-87536-9_23 – ident: ref9 doi: 10.1145/2980258.2980319 – volume: 80 start-page: 4393 volume-title: Proc. 35th Int. Conf. Mach. Learn. ident: ref77 article-title: Deep one-class classification – ident: ref10 doi: 10.1109/ICCIC.2015.7435726 – ident: ref67 doi: 10.1109/CVPR.2019.00301 – year: 2009 ident: ref61 article-title: Learning multiple layers of features from tiny images – ident: ref45 doi: 10.1109/ICVGIP.2008.47 – ident: ref64 doi: 10.1109/CVPR.2018.00356 – ident: ref3 doi: 10.1109/TKDE.2008.239 – ident: ref75 doi: 10.1109/ICCV48922.2021.00867 – ident: ref52 doi: 10.1109/CVPR.2018.00907 – ident: ref80 doi: 10.1109/TCSVT.2020.3046505 – volume: 31 start-page: 1 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref78 article-title: Deep anomaly detection using geometric transformations – year: 2021 ident: ref79 article-title: One-class meta-learning: Towards generalizable few-shot open-set classification publication-title: arXiv:2109.06859 – ident: ref17 doi: 10.1016/j.patcog.2013.06.005 – ident: ref31 doi: 10.1109/TPAMI.2009.98 – ident: ref47 doi: 10.1007/s11263-009-0275-4 – year: 2007 ident: ref59 article-title: Caltech256 image dataset – ident: ref92 doi: 10.1109/GlobalSIP.2015.7418328 – start-page: 1 volume-title: Proc. CAp ident: ref38 article-title: Using SVDD in SimpleMKL for 3D-shapes filtering – ident: ref29 doi: 10.1109/TPAMI.2019.2895608 – ident: ref58 doi: 10.1109/LSP.2018.2889273 – ident: ref6 doi: 10.1109/TIFS.2008.2008216 – year: 2001 ident: ref13 article-title: One-class classification: Concept-learning in the absence of counter-examples – ident: ref4 doi: 10.1109/ACCESS.2017.2729161 – ident: ref20 doi: 10.1109/CVPR.2013.433 – ident: ref21 doi: 10.1109/ICPR.2018.8546301 – ident: ref56 doi: 10.1109/CVPR.2006.100 – ident: ref69 doi: 10.1109/CVPR46437.2021.01466 – volume-title: CVX: MATLAB Software for Disciplined Convex Programming, Version 2.1 year: 2014 ident: ref88 – ident: ref84 doi: 10.1016/j.neucom.2020.08.068 – year: 2014 ident: ref50 article-title: Very deep convolutional networks for large-scale image recognition publication-title: arXiv:1409.1556 – year: 2018 ident: ref62 article-title: Meta-learning for semi-supervised few-shot classification publication-title: arXiv:1803.00676 – volume: 7 start-page: 1531 year: 2006 ident: ref39 article-title: Large scale multiple kernel learning publication-title: J. Mach. Learn. Res. – ident: ref40 doi: 10.1016/j.patcog.2012.09.002 – ident: ref53 doi: 10.1109/CVPR.2018.00474 – ident: ref7 doi: 10.1109/TII.2014.2330796 – ident: ref66 doi: 10.1109/CVPR.2017.460 – ident: ref22 doi: 10.1109/TNNLS.2020.2979823 – ident: ref72 doi: 10.1109/CVPR42600.2020.01419 – ident: ref83 doi: 10.1109/FG.2017.77 – year: 2020 ident: ref85 article-title: Learning generalized spoof cues for face anti-spoofing publication-title: arXiv:2005.03922 – start-page: 73 volume-title: Proc. Wein 8th Ergebn. Math. Kolloq. ident: ref43 article-title: Uber ein okonomisches gleichungssystem und eine verallgemeinerung des brouwerschen fixpunktsatzes – volume: 25 start-page: 1097 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref51 article-title: ImageNet classification with deep convolutional neural networks – ident: ref35 doi: 10.1109/TNNLS.2017.2785329 – ident: ref76 doi: 10.5555/3294996.3295163 – year: 2018 ident: ref73 article-title: Novelty detection with GAN publication-title: arXiv:1802.10560 – ident: ref26 doi: 10.1109/TPAMI.2013.212 – ident: ref68 doi: 10.1109/CVPR.2019.00057 – ident: ref12 doi: 10.1109/TCYB.2018.2838668 – ident: ref91 doi: 10.1002/cpe.2933 – ident: ref15 doi: 10.1609/aaai.v33i01.33013991 – ident: ref87 doi: 10.1109/TIFS.2020.2985530 – volume-title: MNIST Handwritten Digit Database year: 2010 ident: ref60 – ident: ref28 doi: 10.1109/TPAMI.2010.183 – ident: ref55 doi: 10.1109/TIP.2019.2917862 – ident: ref19 doi: 10.1109/CVPR.2017.439 – volume: 33 start-page: 19111 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref74 article-title: HRN: A holistic approach to one class learning – ident: ref2 doi: 10.1017/S026988891300043X
SSID	ssj0014516
Score	2.4322436
Snippet	We address the one-class classification (OCC) problem and advocate a one-class MKL (multiple kernel learning) approach for this purpose. To this aim, based on...
SourceID	proquest pubmed crossref ieee
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	1
SubjectTerms	Algorithms Classification Kernel Kernels Machine learning multiple kernel learning One-class classification one-class Fisher null transformation Optimization Pattern recognition Regularization Saddle points Search problems Support vector machines Task analysis Training ℓ<sub xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> p -norm regularisation
Title	One-Class Classification Using ℓp-Norm Multiple Kernel Fisher Null Approach
URI	https://ieeexplore.ieee.org/document/10070580 https://www.ncbi.nlm.nih.gov/pubmed/37028349 https://www.proquest.com/docview/2789481980 https://www.proquest.com/docview/2798708313
Volume	32
WOSCitedRecordID	wos000957597000001&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: 1941-0042 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014516 issn: 1057-7149 databaseCode: RIE dateStart: 19920101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NTtwwEB61qAd6AEopXf7kSlx68G4S27F9RKgIVLHlANLeoiQeS0goi_anT8Ab8IY8CWPHu-JCpV6iKHYSyzOTfOPxzAdwao1xjnAC1ypHLr3KedP4kqOUsihcaYRoI9mEHo_NZGJvUrJ6zIVBxLj5DIfhNMby3bRdhqWyUYjoZ8qQh_5R67JP1lqHDALjbAxtKs014f5VTDKzo9urm2GgCR8KAtB5WkFZ_YMiqcr7-DL-Zy62_3OEO7CVACU76zXgC3zAbhe2E7hkyXTnu_D5TeXBr3D9p0MeCTFZPIYNQ1FGLO4hYC9Pz498THiWXacdh-w3zjp8YD1XOhuT68rOUkHyPbi7-HV7fskTswJvhTQL3kjvhcK8zTPvnHXeuAxr32SFaxpTyxoLXRtJ7iDhN1nTJVNItE6pOlemdOIbbHTTDr8Ds6X2Wtqs9SRdbRrSCZc5T3aOKhTHG8BoNddVm8qOB_aLhyq6H5mtSDpVkE6VpDOAn-s7HvuSG__ouxeE8KZfP_8DOFrJs0pGOa9C0q8kBBSaf6ybyZxCjKTucLoMfSx9wYzIxQD2ez1YP1zoAMakPXjnpYewGcbWL9AcwcZitsRj-NT-XdzPZyeksxNzEnX2FSZ85rs
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NTtwwEB5VFKntASgFuoW2rtRLD95NYnttHxEqAsGmHLYStyiJxxISyqL96RP0DfqGfRLGjnfFhUq9RFHsJJZnJvnG45kP4Ks1xjnCCVyrHLn0KudN48ccpZRF4cZGiDaSTeiyNLe39iYlq8dcGESMm89wGE5jLN_N2lVYKhuFiH6mDHnoL5Ukx6dP19oEDQLnbAxuKs01If91VDKzo-nlzTAQhQ8FQeg8raGs_0KRVuV5hBn_NOe7_znGPdhJkJKd9jrwFl5gtw-7CV6yZLyLfXjzpPbgO5j86JBHSkwWj2HLUJQSi7sI2N_ffx54SYiWTdKeQ3aF8w7vWc-WzkpyXtlpKkl-AD_Pv0_PLnjiVuCtkGbJG-m9UJi3eeads84bl2Htm6xwTWNqWWOhayPJISQEJ2u6ZAqJ1ilV58qMnTiErW7W4Xtgdqy9ljZrPclXm4a0wmXOk6WjCuXxBjBaz3XVpsLjgf_ivooOSGYrkk4VpFMl6Qzg2-aOh77oxj_6HgQhPOnXz_8ATtbyrJJZLqqQ9isJA4XmL5tmMqgQJak7nK1CH0vfMCNyMYCjXg82Dxc6wDFpPzzz0s_w6mI6ua6uL8urY3gdxtkv15zA1nK-wo-w3f5a3i3mn6LmPgLbZ-ka
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=One-Class+Classification+Using+%E2%84%93p-Norm+Multiple+Kernel+Fisher+Null+Approach&rft.jtitle=IEEE+transactions+on+image+processing&rft.au=Rahimzadeh+Arashloo%2C+Shervin&rft.date=2023-01-01&rft.issn=1941-0042&rft.eissn=1941-0042&rft.volume=32&rft.spage=1843&rft_id=info:doi/10.1109%2FTIP.2023.3255102&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1057-7149&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1057-7149&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1057-7149&client=summon