D.C. programming for sparse proximal support vector machines
•New SPSVMs model is proposed by using l0-norm rather than l1-norm.•Introduce a new nonconvex continuous approximation of l0-norm.•An alternating scheme based on DCA is proposed for SPSVMs.•Preliminary experimental results are presented to show the efficiency of the proposed method. Proximal support...
Saved in:
| Published in: | Information sciences Vol. 547; pp. 187 - 201 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Inc
08.02.2021
|
| Subjects: | |
| ISSN: | 0020-0255, 1872-6291 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | •New SPSVMs model is proposed by using l0-norm rather than l1-norm.•Introduce a new nonconvex continuous approximation of l0-norm.•An alternating scheme based on DCA is proposed for SPSVMs.•Preliminary experimental results are presented to show the efficiency of the proposed method.
Proximal support vector machine (PSVM), as a variant of support vector machine (SVM), is to generate a pair of non-parallel hyperplanes for classification. Although PSVM is one of the powerful classification tools, its ability on feature selection is still weak. To overcome this defect, we introduce ℓ0-norm regularization in PSVM which enables PSVM to select important features and remove redundant features simultaneously for classification. This PSVM is called as a sparse proximal support vector machine (SPSVM). Due to the presence of ℓ0-norm, the resulting optimization problem of SPSVM is neither convex nor smooth and thus, is difficult to solve. In this paper, we introduce a continuous nonconvex function to approximate ℓ0-norm, and propose a novel difference of convex functions algorithms (DCA) to solve SPSVM. The main merit of the proposed method is that all subproblems are smooth and admit closed form solutions. The effectiveness of the proposed method is illustrated by theoretical analysis as well as some numerical experiments on both simulation datasets and real world datasets. |
|---|---|
| AbstractList | •New SPSVMs model is proposed by using l0-norm rather than l1-norm.•Introduce a new nonconvex continuous approximation of l0-norm.•An alternating scheme based on DCA is proposed for SPSVMs.•Preliminary experimental results are presented to show the efficiency of the proposed method.
Proximal support vector machine (PSVM), as a variant of support vector machine (SVM), is to generate a pair of non-parallel hyperplanes for classification. Although PSVM is one of the powerful classification tools, its ability on feature selection is still weak. To overcome this defect, we introduce ℓ0-norm regularization in PSVM which enables PSVM to select important features and remove redundant features simultaneously for classification. This PSVM is called as a sparse proximal support vector machine (SPSVM). Due to the presence of ℓ0-norm, the resulting optimization problem of SPSVM is neither convex nor smooth and thus, is difficult to solve. In this paper, we introduce a continuous nonconvex function to approximate ℓ0-norm, and propose a novel difference of convex functions algorithms (DCA) to solve SPSVM. The main merit of the proposed method is that all subproblems are smooth and admit closed form solutions. The effectiveness of the proposed method is illustrated by theoretical analysis as well as some numerical experiments on both simulation datasets and real world datasets. |
| Author | Wu, Zhiyou Yang, Linxi Wu, Changzhi Li, Guoquan |
| Author_xml | – sequence: 1 givenname: Guoquan orcidid: 0000-0002-3731-6848 surname: Li fullname: Li, Guoquan organization: School of Mathematical Science, Chongqing Normal University, Chonqing 401331, China – sequence: 2 givenname: Linxi surname: Yang fullname: Yang, Linxi organization: School of Mathematical Science, Chongqing Normal University, Chonqing 401331, China – sequence: 3 givenname: Zhiyou surname: Wu fullname: Wu, Zhiyou organization: School of Mathematical Science, Chongqing Normal University, Chonqing 401331, China – sequence: 4 givenname: Changzhi orcidid: 0000-0002-2276-6862 surname: Wu fullname: Wu, Changzhi email: changzhiwu@gzhu.edu.cn organization: School of Management, Guangzhou University, Guangzhou 510006, China |
| BookMark | eNp9kM1KAzEUhYNUsK0-gLt5gYk3mclP0Y3UXyi40XVIM0lN6SRDMhZ9e1PqykVXl3sO3-WeM0OTEINF6JoAJkD4zRb7kDEFChgkhkaeoSmRgtacLsgETaE4NVDGLtAs5y0AtILzKbp7wEtcDSluku57HzaVi6nKg07ZHuRv3-tdlb-GIaax2lszFrvX5tMHmy_RudO7bK_-5hx9PD2-L1_q1dvz6_J-VRu6EGPdNroVbM07zcHRjjVaSqelWDNDSHmMWMbassBCN7So65YL4pykjjrhNGnmSBzvmhRzTtYp40c9-hjGpP1OEVCHEtRWlRLUoQQFUpUSCkn-kUMqidLPSeb2yNgSae9tUtl4G4ztfCr5VRf9CfoXdFV25w |
| CitedBy_id | crossref_primary_10_1016_j_apnum_2023_04_004 crossref_primary_10_1007_s42835_024_01991_9 crossref_primary_10_1016_j_ins_2023_119986 crossref_primary_10_1109_ACCESS_2023_3290020 crossref_primary_10_1016_j_sigpro_2022_108915 crossref_primary_10_1016_j_eswa_2024_123378 crossref_primary_10_1080_02331934_2021_1960331 crossref_primary_10_1088_1361_6501_ad2ad6 crossref_primary_10_1016_j_asoc_2022_109506 crossref_primary_10_1016_j_ins_2020_11_033 crossref_primary_10_1016_j_measurement_2022_111337 crossref_primary_10_1007_s10915_025_02900_6 crossref_primary_10_1007_s13042_021_01368_8 crossref_primary_10_1109_TIM_2025_3552866 crossref_primary_10_1155_2022_5255346 crossref_primary_10_1109_ACCESS_2025_3555580 crossref_primary_10_3390_ma15238504 crossref_primary_10_1016_j_ins_2024_120591 crossref_primary_10_1016_j_neucom_2025_129438 crossref_primary_10_3233_JIFS_211631 crossref_primary_10_1016_j_patcog_2022_108779 crossref_primary_10_1016_j_ins_2024_120461 crossref_primary_10_1016_j_patcog_2022_108976 crossref_primary_10_1016_j_asoc_2021_108231 |
| Cites_doi | 10.1016/j.patcog.2011.03.031 10.3934/jimo.2014.10.817 10.1109/TFUZZ.2019.2893863 10.1023/A:1022627411411 10.1016/j.eswa.2011.01.131 10.1007/s13042-019-00984-9 10.1007/s00500-008-0323-y 10.1007/s13042-019-00952-3 10.1016/j.ins.2014.01.041 10.1016/j.patrec.2008.05.016 10.1109/TNN.2011.2130540 10.1109/LSP.2012.2216874 10.1007/s13042-018-0892-8 10.1080/10556780600883874 10.1016/j.patcog.2018.01.016 10.1007/s13042-019-01028-y 10.1109/TPAMI.2007.1068 10.1145/1961189.1961199 10.1109/TMI.2016.2547947 10.1016/j.eswa.2015.08.022 10.1016/j.ins.2017.11.035 10.1073/pnas.96.12.6745 10.1137/1.9781611972757.50 10.1007/s10107-018-1235-y 10.1109/TPAMI.2006.17 10.1016/j.engappai.2019.103397 10.1021/jf950305a 10.1016/j.neunet.2009.08.001 10.1007/s13042-019-00957-y 10.1023/A:1009715923555 10.1016/j.patcog.2006.07.010 10.1016/B978-1-55860-377-6.50071-2 10.1080/02331934.2011.611515 10.1007/s10107-017-1144-5 10.1109/CISIS.2010.116 10.1007/s13042-019-00936-3 10.1007/s00521-012-1331-5 10.1080/02331934.2014.994627 |
| ContentType | Journal Article |
| Copyright | 2020 Elsevier Inc. |
| Copyright_xml | – notice: 2020 Elsevier Inc. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.ins.2020.08.038 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Library & Information Science |
| EISSN | 1872-6291 |
| EndPage | 201 |
| ExternalDocumentID | 10_1016_j_ins_2020_08_038 S0020025520308057 |
| GroupedDBID | --K --M --Z -~X .DC .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXUO AAYFN ABAOU ABBOA ABFNM ABJNI ABMAC ABUCO ABYKQ ACAZW ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE ADGUI ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIGVJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ARUGR AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W JJJVA KOM LG9 LY1 M41 MHUIS MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 ROL RPZ SDF SDG SDP SES SPC SPCBC SSB SSD SST SSV SSW SSZ T5K TN5 TWZ WH7 XPP ZMT ~02 ~G- 1OL 29I 77I 9DU AAAKG AAQXK AATTM AAXKI AAYWO AAYXX ABEFU ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO ADVLN AEIPS AEUPX AFFNX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB HLZ HVGLF HZ~ H~9 R2- SBC SDS SEW UHS WUQ YYP ZY4 ~HD |
| ID | FETCH-LOGICAL-c297t-43a475b6da60f2d53a88fa87b5c110021e554b5c09a327b5b4671ff82f2f7fa13 |
| ISICitedReferencesCount | 28 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000590678600011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0020-0255 |
| IngestDate | Tue Nov 18 22:10:31 EST 2025 Sat Nov 29 07:27:57 EST 2025 Fri Feb 23 02:45:54 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | DC programming Support vector machine Sparse proximal support vector machine DC Algorithm |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c297t-43a475b6da60f2d53a88fa87b5c110021e554b5c09a327b5b4671ff82f2f7fa13 |
| ORCID | 0000-0002-2276-6862 0000-0002-3731-6848 |
| PageCount | 15 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_ins_2020_08_038 crossref_primary_10_1016_j_ins_2020_08_038 elsevier_sciencedirect_doi_10_1016_j_ins_2020_08_038 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-02-08 |
| PublicationDateYYYYMMDD | 2021-02-08 |
| PublicationDate_xml | – month: 02 year: 2021 text: 2021-02-08 day: 08 |
| PublicationDecade | 2020 |
| PublicationTitle | Information sciences |
| PublicationYear | 2021 |
| Publisher | Elsevier Inc |
| Publisher_xml | – name: Elsevier Inc |
| References | Yu, Xu, Wang (b0050) 2020; 11 Cortes, Vapnik (b0010) 1995; 20 Jayadeva, Khemchandani, Chandra (b0065) 2007; 29 Guarracino, Cifarelli, Seref, Pardalos (b0125) 2007; 22 Maldonado, López (b0020) 2014; 268 Pham Dinh, Le Thi (b0170) 1997; 22 Shao, Zhang, Wang, Deng (b0090) 2011; 22 Weston, Elisseeff, Scholkopf, Tipping (b0150) 2003; 3 Hao (b0055) 2010; 23 Burges (b0045) 1998; 2 Huang, Yu, Gu, Zhang, Cen (b0030) 2019; 10 Alon, Barkai, Notterman, Gish, Ybarra, Mack (b0200) 1999; 96 S.M. Lee, D.S. Kim, J.H. Kim, J.S. Park, Spam detection using feature selection and parameters optimization, in: 2010 International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), IEEE, 2010, pp. 883–888 Schlimmer (b0205) 1981 Gu, Chung, Wang (b0040) 2020; 11 Bai, Niu, Chen (b0005) 2013; 62 Liu, Lai, Ou (b0135) 2020 Don, Iacob (b0035) 2020; 11 Peng, Chen (b0105) 2019; 10 Shao, Li, Liu, Wang, Deng (b0130) 2018; 78 W.N. Street, W.H. Wolberg, O.L. Mangasarian, Nuclear feature extraction for breast tumor diagnosis, in: Biomedical Image Processing and Biomedical Visualization, International Society for Optics and Photonics, 1993, pp. 861–870 Kohavi (b0180) 1995; 14 Le Thi, Pham Dinh (b0160) 2018; 169 Mesejo (b0195) 2016; 35 Wang, Gao, Zhao, Chen (b0100) 2020; 11 Ahmadi, Hall (b0155) 2017; 169 Peng (b0080) 2011; 44 Tian, Ju, Qi (b0025) 2014; 24 López, Maldonado, Carrasco (b0145) 2017; 429 Deng, Tian, Zhang (b0015) 2012 Ye, Zhao, Zhang, Ye (b0095) 2011; 38 Dudul (b0215) 2007; 88 Pappu, Panagopoulos, Xanthopoulos, Pardalos (b0120) 2015; 42 Rezvani, Wang, Pourpanah (b0110) 2019; 27 Mangasarian (b0140) 2006; 7 Briandet, Kemsley, Wilson (b0190) 1996; 44 W.N. Street, O.L. Mangasarian, W.H. Wolberg, An inductive learning approach to prognostic prediction, in: ICML, Citeseer, 1995, pp. 522–530 Li, Shao, Deng (b0070) 2016; 65 Li, Ren, Shao, Ye, Guo (b0115) 2020; 88 Mamitsuka (b0235) 2006; 39 Kumar, Gopal (b0060) 2008; 29 Chang, Lin (b0175) 2011; 2 Alcal-Fdez, Snchez (b0230) 2009; 13 Shao, Deng, Chen, Wang (b0085) 2013; 20 Wu, Li, Long (b0165) 2014; 10 Mangasarian, Wild (b0075) 2006; 28 C.A. Ratanamahatana, E. Keogh, Three myths about dynamic time warping data mining, Proceedings of the 2005 SIAM International Conference on Data Mining, SDM 2005 Huang (10.1016/j.ins.2020.08.038_b0030) 2019; 10 Hao (10.1016/j.ins.2020.08.038_b0055) 2010; 23 Alcal-Fdez (10.1016/j.ins.2020.08.038_b0230) 2009; 13 Li (10.1016/j.ins.2020.08.038_b0115) 2020; 88 Shao (10.1016/j.ins.2020.08.038_b0090) 2011; 22 Guarracino (10.1016/j.ins.2020.08.038_b0125) 2007; 22 Burges (10.1016/j.ins.2020.08.038_b0045) 1998; 2 Dudul (10.1016/j.ins.2020.08.038_b0215) 2007; 88 Kohavi (10.1016/j.ins.2020.08.038_b0180) 1995; 14 Mangasarian (10.1016/j.ins.2020.08.038_b0075) 2006; 28 Wang (10.1016/j.ins.2020.08.038_b0100) 2020; 11 Briandet (10.1016/j.ins.2020.08.038_b0190) 1996; 44 Pappu (10.1016/j.ins.2020.08.038_b0120) 2015; 42 Le Thi (10.1016/j.ins.2020.08.038_b0160) 2018; 169 Pham Dinh (10.1016/j.ins.2020.08.038_b0170) 1997; 22 Yu (10.1016/j.ins.2020.08.038_b0050) 2020; 11 Mangasarian (10.1016/j.ins.2020.08.038_b0140) 2006; 7 Kumar (10.1016/j.ins.2020.08.038_b0060) 2008; 29 Li (10.1016/j.ins.2020.08.038_b0070) 2016; 65 Ye (10.1016/j.ins.2020.08.038_b0095) 2011; 38 Alon (10.1016/j.ins.2020.08.038_b0200) 1999; 96 Shao (10.1016/j.ins.2020.08.038_b0085) 2013; 20 10.1016/j.ins.2020.08.038_b0210 Mesejo (10.1016/j.ins.2020.08.038_b0195) 2016; 35 Tian (10.1016/j.ins.2020.08.038_b0025) 2014; 24 Gu (10.1016/j.ins.2020.08.038_b0040) 2020; 11 Rezvani (10.1016/j.ins.2020.08.038_b0110) 2019; 27 Don (10.1016/j.ins.2020.08.038_b0035) 2020; 11 Weston (10.1016/j.ins.2020.08.038_b0150) 2003; 3 Peng (10.1016/j.ins.2020.08.038_b0080) 2011; 44 Ahmadi (10.1016/j.ins.2020.08.038_b0155) 2017; 169 Wu (10.1016/j.ins.2020.08.038_b0165) 2014; 10 Jayadeva (10.1016/j.ins.2020.08.038_b0065) 2007; 29 López (10.1016/j.ins.2020.08.038_b0145) 2017; 429 Shao (10.1016/j.ins.2020.08.038_b0130) 2018; 78 Bai (10.1016/j.ins.2020.08.038_b0005) 2013; 62 Cortes (10.1016/j.ins.2020.08.038_b0010) 1995; 20 10.1016/j.ins.2020.08.038_b0225 Deng (10.1016/j.ins.2020.08.038_b0015) 2012 Liu (10.1016/j.ins.2020.08.038_b0135) 2020 Maldonado (10.1016/j.ins.2020.08.038_b0020) 2014; 268 Mamitsuka (10.1016/j.ins.2020.08.038_b0235) 2006; 39 10.1016/j.ins.2020.08.038_b0185 Chang (10.1016/j.ins.2020.08.038_b0175) 2011; 2 Peng (10.1016/j.ins.2020.08.038_b0105) 2019; 10 10.1016/j.ins.2020.08.038_b0220 Schlimmer (10.1016/j.ins.2020.08.038_b0205) 1981 |
| References_xml | – reference: C.A. Ratanamahatana, E. Keogh, Three myths about dynamic time warping data mining, Proceedings of the 2005 SIAM International Conference on Data Mining, SDM 2005 – volume: 13 start-page: 307 year: 2009 end-page: 318 ident: b0230 article-title: KEEL: a software tool to assess evolutionary algorithms to data mining problems publication-title: Soft Comput. – volume: 27 start-page: 2140 year: 2019 end-page: 2151 ident: b0110 article-title: Intuitionistic fuzzy twin support vector machines publication-title: IEEE Trans. Fuzzy Syst. – year: 2012 ident: b0015 article-title: Support Vector Machines: Theory, Algorithms, and Extensions – volume: 11 start-page: 715 year: 2020 end-page: 728 ident: b0050 article-title: Bibliometric analysis of support vector machines research trend: a case study in China publication-title: Int. J. Mach. Learn. Cybern. – volume: 429 start-page: 377 year: 2017 end-page: 389 ident: b0145 article-title: Double regularization methods for robust feature selection and SVM classification via DC programming publication-title: Inf. Sci. – volume: 11 start-page: 433 year: 2020 end-page: 447 ident: b0035 article-title: DCSVM: fast multi-class classification using support vector machines publication-title: Int. J. Mach. Learn. Cybern. – volume: 62 start-page: 561 year: 2013 end-page: 562 ident: b0005 article-title: New SDP models for protein homology detection with semi-supervised SVM publication-title: Optimization – volume: 88 start-page: 26 year: 2007 end-page: 41 ident: b0215 article-title: Classification of radar returns from the ionosphere using RBF neural network publication-title: J. Inst. Eng. India Part Electron. Telecommun. Eng. Division – volume: 29 start-page: 1842 year: 2008 end-page: 1848 ident: b0060 article-title: Application of smoothing technique on twin support vector machines publication-title: Pattern Recogn. Lett. – volume: 7 start-page: 1517 year: 2006 end-page: 1530 ident: b0140 article-title: Exact 1-norm support vector machines via unconstrained convex differentiable minimization publication-title: J. Mach. Learn. Res. – volume: 24 start-page: 1089 year: 2014 end-page: 1099 ident: b0025 article-title: Efficient sparse nonparallel support vector machines for classification publication-title: Neural Comput. Appl. – volume: 10 start-page: 2573 year: 2019 end-page: 2588 ident: b0105 article-title: An L_1)norm loss based twin support vector regression and its geometric extension publication-title: Int. J. Mach. Learn. Cybern. – volume: 2 start-page: 27 year: 2011 end-page: 49 ident: b0175 article-title: LIBSVM: a library for support vector machines publication-title: ACM Trans. Intell. Syst. Technol. – volume: 96 start-page: 6745 year: 1999 end-page: 6750 ident: b0200 article-title: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays publication-title: Proc. Nat. Acad. Sci. – volume: 28 start-page: 69 year: 2006 end-page: 74 ident: b0075 article-title: Multisurface proximal support vector machine classification via generalized eigenvalues publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 22 start-page: 962 year: 2011 end-page: 968 ident: b0090 article-title: Improvements on twin support vector machines publication-title: IEEE Trans. Neural Networks – volume: 78 start-page: 167 year: 2018 end-page: 181 ident: b0130 article-title: Sparse ℓ_q)norm least squares support vector machine with feature selection publication-title: Pattern Recogn. – reference: S.M. Lee, D.S. Kim, J.H. Kim, J.S. Park, Spam detection using feature selection and parameters optimization, in: 2010 International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), IEEE, 2010, pp. 883–888 – year: 2020 ident: b0135 article-title: Structured optimal graph based sparse feature extraction for semi-supervised learning publication-title: Signal Process – volume: 88 year: 2020 ident: b0115 article-title: Generalized elastic net Lp-norm nonparallel support vector machine publication-title: Eng. Appl. Artif. Intell. – volume: 23 start-page: 60 year: 2010 end-page: 73 ident: b0055 article-title: New support vector algorithms with parametric insensitive margin model publication-title: Neural Networks – volume: 20 start-page: 213 year: 2013 end-page: 216 ident: b0085 article-title: Improved generalized eigenvalue proximal support vector machine publication-title: IEEE Signal Process. Lett. – volume: 42 start-page: 9183 year: 2015 end-page: 9191 ident: b0120 article-title: Sparse proximal support vector machines for feature selection in high dimensional datasets publication-title: Expert Syst. Appl. – volume: 268 start-page: 328 year: 2014 end-page: 341 ident: b0020 article-title: Alternative second-order cone programming formulations for support vector classification publication-title: Inf. Sci. – volume: 22 start-page: 73 year: 2007 end-page: 81 ident: b0125 article-title: A classification method based on generalized eigenvalue problems publication-title: Optim. Methods Software – reference: W.N. Street, W.H. Wolberg, O.L. Mangasarian, Nuclear feature extraction for breast tumor diagnosis, in: Biomedical Image Processing and Biomedical Visualization, International Society for Optics and Photonics, 1993, pp. 861–870 – volume: 11 start-page: 95 year: 2020 end-page: 110 ident: b0100 article-title: Wavelet transform-based weighted publication-title: Int. J. Mach. Learn. Cybern. – volume: 39 start-page: 2393 year: 2006 end-page: 2404 ident: b0235 article-title: Selecting features in microarray classification using roc curves publication-title: Pattern Recogn. – volume: 44 start-page: 170 year: 1996 end-page: 174 ident: b0190 article-title: Discrimination of arabica and robusta in instant coffee by fourier transform infrared spectroscopy and chemometrics publication-title: J. Agric. Food Chem. – volume: 11 start-page: 33 year: 2020 end-page: 53 ident: b0040 article-title: Extreme vector machine for fast training on large data publication-title: Int. J. Mach. Learn. Cybern. – volume: 65 start-page: 169 year: 2016 end-page: 183 ident: b0070 article-title: Robust L1-norm non-parallel proximal support vector machine publication-title: Optimization – volume: 169 start-page: 69 year: 2017 end-page: 94 ident: b0155 article-title: DC decomposition of non-convex polynomials with algebraic techniques publication-title: Math. Program. – volume: 2 start-page: 1 year: 1998 end-page: 43 ident: b0045 article-title: A tutorial on support vector machines for pattern recognition publication-title: Data Min. Knowl. Discovery – volume: 44 start-page: 2678 year: 2011 end-page: 2692 ident: b0080 article-title: TPMSVM: a novel twin parametric-margin support vector machine for pattern recognition publication-title: Pattern Recogn. – volume: 10 start-page: 817 year: 2014 end-page: 826 ident: b0165 article-title: A DC programming approach for sensor network localization with uncertainties in anchor positions publication-title: J. Ind. Manage. Optim. – volume: 38 start-page: 9425 year: 2011 end-page: 9433 ident: b0095 article-title: Distance difference and linear programming nonparallel plane classifier publication-title: Expert Syst. Appl. – volume: 20 start-page: 273 year: 1995 end-page: 297 ident: b0010 article-title: Support vector networks publication-title: Mach. Learn. – volume: 10 start-page: 3667 year: 2019 end-page: 3686 ident: b0030 article-title: Sparse and heuristic support vector machine for binary classifier and regressor fusion publication-title: Int. J. Mach. Learn. Cybern. – year: 1981 ident: b0205 article-title: Mushroom Records Drawn from the Audubon Society Field Guide to North American Mushrooms – reference: W.N. Street, O.L. Mangasarian, W.H. Wolberg, An inductive learning approach to prognostic prediction, in: ICML, Citeseer, 1995, pp. 522–530 – volume: 29 start-page: 905 year: 2007 end-page: 910 ident: b0065 article-title: Twin support vector machines for pattern classification publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 3 start-page: 1439 year: 2003 end-page: 1461 ident: b0150 article-title: Use of the zero-norm with linear models and kernel methods publication-title: J. Mach. Learn. Res. – volume: 14 start-page: 1137 year: 1995 end-page: 1145 ident: b0180 article-title: A study of cross-validation and bootstrap for accuracy estimation and model selection publication-title: International Joint Conference on Artificial Intelligence – volume: 22 start-page: 289 year: 1997 end-page: 355 ident: b0170 article-title: Convex analysis approach to D.C. programming: theory, algorithms and applications publication-title: Acta Math. Vietnam. – volume: 35 start-page: 2051 year: 2016 end-page: 2063 ident: b0195 article-title: Computer-aided classification of gastrointestinal lesions in regular colonoscopy publication-title: IEEE Trans. Med. Imag. – volume: 169 start-page: 5 year: 2018 end-page: 68 ident: b0160 article-title: DC programming and DCA: thirty years of developments publication-title: Math. Program. – volume: 44 start-page: 2678 issue: 10 year: 2011 ident: 10.1016/j.ins.2020.08.038_b0080 article-title: TPMSVM: a novel twin parametric-margin support vector machine for pattern recognition publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2011.03.031 – year: 2020 ident: 10.1016/j.ins.2020.08.038_b0135 article-title: Structured optimal graph based sparse feature extraction for semi-supervised learning publication-title: Signal Process – volume: 10 start-page: 817 issue: 3 year: 2014 ident: 10.1016/j.ins.2020.08.038_b0165 article-title: A DC programming approach for sensor network localization with uncertainties in anchor positions publication-title: J. Ind. Manage. Optim. doi: 10.3934/jimo.2014.10.817 – volume: 22 start-page: 289 year: 1997 ident: 10.1016/j.ins.2020.08.038_b0170 article-title: Convex analysis approach to D.C. programming: theory, algorithms and applications publication-title: Acta Math. Vietnam. – volume: 27 start-page: 2140 issue: 11 year: 2019 ident: 10.1016/j.ins.2020.08.038_b0110 article-title: Intuitionistic fuzzy twin support vector machines publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2019.2893863 – volume: 20 start-page: 273 year: 1995 ident: 10.1016/j.ins.2020.08.038_b0010 article-title: Support vector networks publication-title: Mach. Learn. doi: 10.1023/A:1022627411411 – volume: 38 start-page: 9425 year: 2011 ident: 10.1016/j.ins.2020.08.038_b0095 article-title: Distance difference and linear programming nonparallel plane classifier publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.01.131 – volume: 11 start-page: 433 year: 2020 ident: 10.1016/j.ins.2020.08.038_b0035 article-title: DCSVM: fast multi-class classification using support vector machines publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-019-00984-9 – volume: 13 start-page: 307 issue: 3 year: 2009 ident: 10.1016/j.ins.2020.08.038_b0230 article-title: KEEL: a software tool to assess evolutionary algorithms to data mining problems publication-title: Soft Comput. doi: 10.1007/s00500-008-0323-y – volume: 10 start-page: 3667 year: 2019 ident: 10.1016/j.ins.2020.08.038_b0030 article-title: Sparse and heuristic support vector machine for binary classifier and regressor fusion publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-019-00952-3 – volume: 268 start-page: 328 year: 2014 ident: 10.1016/j.ins.2020.08.038_b0020 article-title: Alternative second-order cone programming formulations for support vector classification publication-title: Inf. Sci. doi: 10.1016/j.ins.2014.01.041 – volume: 29 start-page: 1842 issue: 13 year: 2008 ident: 10.1016/j.ins.2020.08.038_b0060 article-title: Application of smoothing technique on twin support vector machines publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2008.05.016 – volume: 22 start-page: 962 issue: 6 year: 2011 ident: 10.1016/j.ins.2020.08.038_b0090 article-title: Improvements on twin support vector machines publication-title: IEEE Trans. Neural Networks doi: 10.1109/TNN.2011.2130540 – volume: 20 start-page: 213 year: 2013 ident: 10.1016/j.ins.2020.08.038_b0085 article-title: Improved generalized eigenvalue proximal support vector machine publication-title: IEEE Signal Process. Lett. doi: 10.1109/LSP.2012.2216874 – volume: 10 start-page: 2573 year: 2019 ident: 10.1016/j.ins.2020.08.038_b0105 article-title: An L_1)norm loss based twin support vector regression and its geometric extension publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-018-0892-8 – volume: 22 start-page: 73 issue: 1 year: 2007 ident: 10.1016/j.ins.2020.08.038_b0125 article-title: A classification method based on generalized eigenvalue problems publication-title: Optim. Methods Software doi: 10.1080/10556780600883874 – volume: 78 start-page: 167 year: 2018 ident: 10.1016/j.ins.2020.08.038_b0130 article-title: Sparse ℓ_q)norm least squares support vector machine with feature selection publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2018.01.016 – volume: 11 start-page: 715 year: 2020 ident: 10.1016/j.ins.2020.08.038_b0050 article-title: Bibliometric analysis of support vector machines research trend: a case study in China publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-019-01028-y – volume: 29 start-page: 905 issue: 5 year: 2007 ident: 10.1016/j.ins.2020.08.038_b0065 article-title: Twin support vector machines for pattern classification publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2007.1068 – volume: 2 start-page: 27 issue: 3 year: 2011 ident: 10.1016/j.ins.2020.08.038_b0175 article-title: LIBSVM: a library for support vector machines publication-title: ACM Trans. Intell. Syst. Technol. doi: 10.1145/1961189.1961199 – volume: 35 start-page: 2051 issue: 9 year: 2016 ident: 10.1016/j.ins.2020.08.038_b0195 article-title: Computer-aided classification of gastrointestinal lesions in regular colonoscopy publication-title: IEEE Trans. Med. Imag. doi: 10.1109/TMI.2016.2547947 – year: 2012 ident: 10.1016/j.ins.2020.08.038_b0015 – volume: 14 start-page: 1137 year: 1995 ident: 10.1016/j.ins.2020.08.038_b0180 article-title: A study of cross-validation and bootstrap for accuracy estimation and model selection publication-title: International Joint Conference on Artificial Intelligence – volume: 42 start-page: 9183 year: 2015 ident: 10.1016/j.ins.2020.08.038_b0120 article-title: Sparse proximal support vector machines for feature selection in high dimensional datasets publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2015.08.022 – volume: 429 start-page: 377 year: 2017 ident: 10.1016/j.ins.2020.08.038_b0145 article-title: Double regularization methods for robust feature selection and SVM classification via DC programming publication-title: Inf. Sci. doi: 10.1016/j.ins.2017.11.035 – volume: 96 start-page: 6745 issue: 12 year: 1999 ident: 10.1016/j.ins.2020.08.038_b0200 article-title: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays publication-title: Proc. Nat. Acad. Sci. doi: 10.1073/pnas.96.12.6745 – ident: 10.1016/j.ins.2020.08.038_b0185 doi: 10.1137/1.9781611972757.50 – volume: 3 start-page: 1439 year: 2003 ident: 10.1016/j.ins.2020.08.038_b0150 article-title: Use of the zero-norm with linear models and kernel methods publication-title: J. Mach. Learn. Res. – volume: 169 start-page: 5 issue: 1 year: 2018 ident: 10.1016/j.ins.2020.08.038_b0160 article-title: DC programming and DCA: thirty years of developments publication-title: Math. Program. doi: 10.1007/s10107-018-1235-y – volume: 28 start-page: 69 year: 2006 ident: 10.1016/j.ins.2020.08.038_b0075 article-title: Multisurface proximal support vector machine classification via generalized eigenvalues publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2006.17 – volume: 88 year: 2020 ident: 10.1016/j.ins.2020.08.038_b0115 article-title: Generalized elastic net Lp-norm nonparallel support vector machine publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2019.103397 – volume: 44 start-page: 170 year: 1996 ident: 10.1016/j.ins.2020.08.038_b0190 article-title: Discrimination of arabica and robusta in instant coffee by fourier transform infrared spectroscopy and chemometrics publication-title: J. Agric. Food Chem. doi: 10.1021/jf950305a – volume: 23 start-page: 60 issue: 1 year: 2010 ident: 10.1016/j.ins.2020.08.038_b0055 article-title: New support vector algorithms with parametric insensitive margin model publication-title: Neural Networks doi: 10.1016/j.neunet.2009.08.001 – volume: 11 start-page: 95 year: 2020 ident: 10.1016/j.ins.2020.08.038_b0100 article-title: Wavelet transform-based weighted ν)twin support vector regression publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-019-00957-y – volume: 7 start-page: 1517 year: 2006 ident: 10.1016/j.ins.2020.08.038_b0140 article-title: Exact 1-norm support vector machines via unconstrained convex differentiable minimization publication-title: J. Mach. Learn. Res. – volume: 2 start-page: 1 year: 1998 ident: 10.1016/j.ins.2020.08.038_b0045 article-title: A tutorial on support vector machines for pattern recognition publication-title: Data Min. Knowl. Discovery doi: 10.1023/A:1009715923555 – volume: 39 start-page: 2393 issue: 12 year: 2006 ident: 10.1016/j.ins.2020.08.038_b0235 article-title: Selecting features in microarray classification using roc curves publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2006.07.010 – ident: 10.1016/j.ins.2020.08.038_b0225 – ident: 10.1016/j.ins.2020.08.038_b0220 doi: 10.1016/B978-1-55860-377-6.50071-2 – year: 1981 ident: 10.1016/j.ins.2020.08.038_b0205 – volume: 62 start-page: 561 year: 2013 ident: 10.1016/j.ins.2020.08.038_b0005 article-title: New SDP models for protein homology detection with semi-supervised SVM publication-title: Optimization doi: 10.1080/02331934.2011.611515 – volume: 169 start-page: 69 issue: 1 year: 2017 ident: 10.1016/j.ins.2020.08.038_b0155 article-title: DC decomposition of non-convex polynomials with algebraic techniques publication-title: Math. Program. doi: 10.1007/s10107-017-1144-5 – ident: 10.1016/j.ins.2020.08.038_b0210 doi: 10.1109/CISIS.2010.116 – volume: 11 start-page: 33 year: 2020 ident: 10.1016/j.ins.2020.08.038_b0040 article-title: Extreme vector machine for fast training on large data publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-019-00936-3 – volume: 24 start-page: 1089 year: 2014 ident: 10.1016/j.ins.2020.08.038_b0025 article-title: Efficient sparse nonparallel support vector machines for classification publication-title: Neural Comput. Appl. doi: 10.1007/s00521-012-1331-5 – volume: 65 start-page: 169 issue: 1 year: 2016 ident: 10.1016/j.ins.2020.08.038_b0070 article-title: Robust L1-norm non-parallel proximal support vector machine publication-title: Optimization doi: 10.1080/02331934.2014.994627 – volume: 88 start-page: 26 year: 2007 ident: 10.1016/j.ins.2020.08.038_b0215 article-title: Classification of radar returns from the ionosphere using RBF neural network publication-title: J. Inst. Eng. India Part Electron. Telecommun. Eng. Division |
| SSID | ssj0004766 |
| Score | 2.4734592 |
| Snippet | •New SPSVMs model is proposed by using l0-norm rather than l1-norm.•Introduce a new nonconvex continuous approximation of l0-norm.•An alternating scheme based... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 187 |
| SubjectTerms | DC Algorithm DC programming Sparse proximal support vector machine Support vector machine |
| Title | D.C. programming for sparse proximal support vector machines |
| URI | https://dx.doi.org/10.1016/j.ins.2020.08.038 |
| Volume | 547 |
| WOSCitedRecordID | wos000590678600011&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-6291 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004766 issn: 0020-0255 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEB6VlAMcEBQQhRbtAXEgcmWvH7uWuFRtgSJU9VCkcLLWj1UdESdK4irtr2f25bgtrQCJi5Ws7Dja-TSvnfkG4F1Z-GFYsMjjsSw9xejlCVoilkURiqCkgudSD5tgJyd8NEpPbXfJQo8TYE3DV6t09l9FjWsobNU6-xfi7n4UF_AzCh2vKHa8_pHgD_cO9lzZ1cTVSaLemC9UT9R0VU9Ui0g7U4738EIn7YcTXVJpywnHrri9a2wcWjvZ-d_fdA3A5xatyhpdP2zqGcPbVd0p-1Yff5zXl9P2-prua7g6r_t5BxroUmW-Toa5hphr9ZrK-_RUmGLMi9GpnFEvoWYol1O6seHZtGozsEbXWGBq0hu3lLvJM4wxIlE869TX3KuGG-YGZ7Y6gtbRElV0POiSPoBNyuKUD2Bz__ho9HXdOsvMcbb73-7gW5cA3njR712Xnjty9hSe2DiC7Bv5P4ONqtmCxz12yS3YtT0p5D3pyZJYbf4cPiqkkB5SCN5EDFKIQwqxSCEGKcQh5QV8_3R0dvDFs8M0vIKmbOlFoYhYnCelSHxJyzgUnEvBWR4XijWQBhU6lvjFT0VIcTVHCxpIyamkkkkRhC9h0Eyb6hUQwQWvWBAVMo8jmaTCF7KSGFdXSVqmfrUNvtunrLBM82rgyc_MlRSOM9zaTG1tpoaghnwbPnSPzAzNyn03R27zM4t_4_9liJS7H3v9b4-9gUdr-O_AYDlvq114WFws68X8rcXTL0eZiz8 |
| linkProvider | Elsevier |
| 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=D.C.+programming+for+sparse+proximal+support+vector+machines&rft.jtitle=Information+sciences&rft.au=Li%2C+Guoquan&rft.au=Yang%2C+Linxi&rft.au=Wu%2C+Zhiyou&rft.au=Wu%2C+Changzhi&rft.date=2021-02-08&rft.pub=Elsevier+Inc&rft.issn=0020-0255&rft.eissn=1872-6291&rft.volume=547&rft.spage=187&rft.epage=201&rft_id=info:doi/10.1016%2Fj.ins.2020.08.038&rft.externalDocID=S0020025520308057 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0020-0255&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0020-0255&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0020-0255&client=summon |