Skip to main content
Erschienen in: International Journal of Machine Learning and Cybernetics 3/2014

01.06.2014 | Original Article

Laplacian smooth twin support vector machine for semi-supervised classification

verfasst von: Wei-Jie Chen, Yuan-Hai Shao, Ning Hong

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 3/2014

Einloggen

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Laplacian twin support vector machine (Lap-TSVM) is a state-of-the-art nonparallel-planes semi-supervised classifier. It tries to exploit the geometrical information embedded in unlabeled data to boost its generalization ability. However, Lap-TSVM may endure heavy burden in training procedure since it needs to solve two quadratic programming problems (QPPs) with the matrix “inversion” operation. In order to enhance the performance of Lap-TSVM, this paper presents a new formulation of Lap-TSVM, termed as Lap-STSVM. Rather than solving two QPPs in dual space, firstly, we convert the primal constrained QPPs of Lap-TSVM into unconstrained minimization problems (UMPs). Afterwards, a smooth technique is introduced to make these UMPs twice differentiable. At last, a fast Newton–Armijo algorithm is designed to solve the UMPs in Lap-STSVM. Experimental evaluation on both artificial and real-world datasets demonstrate the benefits of the proposed approach.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Weitere Produktempfehlungen anzeigen
Fußnoten
1
Matlab is available at http://​www.​mathworks.​com.
 
2
The UCI datasets are available at http://​archive.​ics.​uci.​edu/​ml.
 
Literatur
1.
Zurück zum Zitat Jayadeva, Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Patt Anal Machine Intell 29(5):905-910 Jayadeva, Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Patt Anal Machine Intell 29(5):905-910
2.
Zurück zum Zitat Shao Y, Zhang C, Wang X, Deng N (2011) Improvements on twin support vector machines. IEEE Trans on Neural Netw 22(6):962–968CrossRef Shao Y, Zhang C, Wang X, Deng N (2011) Improvements on twin support vector machines. IEEE Trans on Neural Netw 22(6):962–968CrossRef
3.
Zurück zum Zitat Vapnik VN (1998) Statistical learning theory. Wiley Press, New York Vapnik VN (1998) Statistical learning theory. Wiley Press, New York
4.
Zurück zum Zitat Huang G, Wang D, Lan Y (2011) Extreme learning machines: a survey. Int J Machine Learn Cybern 2(2):107–122CrossRef Huang G, Wang D, Lan Y (2011) Extreme learning machines: a survey. Int J Machine Learn Cybern 2(2):107–122CrossRef
5.
Zurück zum Zitat Deng N, Tian Y, Zhang C (2012) Support vector machines: theory, algorithms and extensions. CRC Press, Philadelphia Deng N, Tian Y, Zhang C (2012) Support vector machines: theory, algorithms and extensions. CRC Press, Philadelphia
6.
Zurück zum Zitat Chen W, Shao Y, Bao W (2012) A novel ensemble TBSVM classifier for imbalanced data classification. J Comput Inf Syst 8(19):8223–8230 Chen W, Shao Y, Bao W (2012) A novel ensemble TBSVM classifier for imbalanced data classification. J Comput Inf Syst 8(19):8223–8230
7.
Zurück zum Zitat Peng X (2011) Building sparse twin support vector machine classifiers in primal space. Inf Sci 181(18):3967–3980CrossRef Peng X (2011) Building sparse twin support vector machine classifiers in primal space. Inf Sci 181(18):3967–3980CrossRef
8.
Zurück zum Zitat Shao Y, Deng N, Yang Z (2012) Least squares recursive projection twin support vector machine for classification. Pattern Recogn 45(6):2299–2307CrossRefMATH Shao Y, Deng N, Yang Z (2012) Least squares recursive projection twin support vector machine for classification. Pattern Recogn 45(6):2299–2307CrossRefMATH
9.
Zurück zum Zitat Yang Z, Shao Y, Zhang X (2013) Multiple birth support vector machine for multi-class classification. Neural Comput Appl 22(1):153–161CrossRef Yang Z, Shao Y, Zhang X (2013) Multiple birth support vector machine for multi-class classification. Neural Comput Appl 22(1):153–161CrossRef
10.
Zurück zum Zitat Peng X (2011) TPMSVM: a novel twin parametric-margin support vector machine for pattern recognition. Pattern Recogn 44(10-11):2678–2692CrossRefMATH Peng X (2011) TPMSVM: a novel twin parametric-margin support vector machine for pattern recognition. Pattern Recogn 44(10-11):2678–2692CrossRefMATH
11.
Zurück zum Zitat Khemchandani R, Karpatne A, Chandra S (2013) Twin support vector regression for the simultaneous learning of a function and its derivatives. Int J Machine Learn Cybern 4(1):51–63 Khemchandani R, Karpatne A, Chandra S (2013) Twin support vector regression for the simultaneous learning of a function and its derivatives. Int J Machine Learn Cybern 4(1):51–63
12.
Zurück zum Zitat Guzella TS, Caminhas WM (2009) A review of machine learning approaches to spam filtering. Exp Syst Appl 36(7):10206–10222CrossRef Guzella TS, Caminhas WM (2009) A review of machine learning approaches to spam filtering. Exp Syst Appl 36(7):10206–10222CrossRef
13.
Zurück zum Zitat Zhang T, Liu S, Xu C, Lu H (2011) Boosted multi-class semi-supervised learning for human action recognition. Pattern Recogn 44(10–11):2334–2342CrossRefMATH Zhang T, Liu S, Xu C, Lu H (2011) Boosted multi-class semi-supervised learning for human action recognition. Pattern Recogn 44(10–11):2334–2342CrossRefMATH
14.
Zurück zum Zitat Maulik U, Chakraborty D (2012) A novel semisupervised SVM for pixel classification of remote sensing imagery. Int J Machine Learn Cybern 3(3):247–258CrossRef Maulik U, Chakraborty D (2012) A novel semisupervised SVM for pixel classification of remote sensing imagery. Int J Machine Learn Cybern 3(3):247–258CrossRef
15.
Zurück zum Zitat Nguyen T, Ho T (2012) Detecting disease genes based on semi-supervised learning and protein–protein interaction networks. Artif Intell Med 54(1):63–71 Nguyen T, Ho T (2012) Detecting disease genes based on semi-supervised learning and protein–protein interaction networks. Artif Intell Med 54(1):63–71
16.
Zurück zum Zitat Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. J Machine Learn Res 7:2399–2434MATHMathSciNet Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. J Machine Learn Res 7:2399–2434MATHMathSciNet
17.
Zurück zum Zitat Melacci S, Belkin M (2011) Laplacian support vector machines trained in the primal. J Machine Learn Res 12:1149–1184MATHMathSciNet Melacci S, Belkin M (2011) Laplacian support vector machines trained in the primal. J Machine Learn Res 12:1149–1184MATHMathSciNet
18.
Zurück zum Zitat Chapelle O, Schölkopf B, Zien A (2010) Semi-supervised learning. MIT Press, Massachusetts Chapelle O, Schölkopf B, Zien A (2010) Semi-supervised learning. MIT Press, Massachusetts
19.
Zurück zum Zitat Wang Y, Chen S, Zhou Z (2012) New semi-supervised classification method based on modified cluster assumption. IEEE Trans Neural Netw Learn Syst 23(5):689–702CrossRefMathSciNet Wang Y, Chen S, Zhou Z (2012) New semi-supervised classification method based on modified cluster assumption. IEEE Trans Neural Netw Learn Syst 23(5):689–702CrossRefMathSciNet
20.
Zurück zum Zitat Zhang S, Lei Y, Wu Y (2011) Semi-supervised locally discriminant projection for classification and recognition. Knowl-Based Syst 24(2):341–346CrossRef Zhang S, Lei Y, Wu Y (2011) Semi-supervised locally discriminant projection for classification and recognition. Knowl-Based Syst 24(2):341–346CrossRef
21.
Zurück zum Zitat Xue H, Chen S, Yang Q (2009) Discriminatively regularized least-squares classification. Pattern Recogn 42(1):93–104MATH Xue H, Chen S, Yang Q (2009) Discriminatively regularized least-squares classification. Pattern Recogn 42(1):93–104MATH
22.
Zurück zum Zitat Soares RGF, Chen H, Yao X (2012) Semisupervised classification with cluster regularization. IEEE Trans Neural Netw Learn Syst 23(11):1779–1792CrossRef Soares RGF, Chen H, Yao X (2012) Semisupervised classification with cluster regularization. IEEE Trans Neural Netw Learn Syst 23(11):1779–1792CrossRef
23.
Zurück zum Zitat Qi Z, Tian Y, Shi Y (2012) Laplacian twin support vector machine for semi-supervised classification. Neural Netw 35:46–53CrossRefMATH Qi Z, Tian Y, Shi Y (2012) Laplacian twin support vector machine for semi-supervised classification. Neural Netw 35:46–53CrossRefMATH
24.
Zurück zum Zitat Chen W, Shao Y, Ye Y (2013) Improving Lap-TSVM with successive overrelaxation and differential evolution. Procedia Comput Sci 17:33–40. Chen W, Shao Y, Ye Y (2013) Improving Lap-TSVM with successive overrelaxation and differential evolution. Procedia Comput Sci 17:33–40.
25.
26.
Zurück zum Zitat Wang Z, Shao Y, Wu T (2013) A GA-based model selection for smooth twin parametric-margin support vector machine. Pattern Recogn 46:2267–2277. Wang Z, Shao Y, Wu T (2013) A GA-based model selection for smooth twin parametric-margin support vector machine. Pattern Recogn 46:2267–2277.
27.
Zurück zum Zitat Kumar MA, Gopal M (2008) Application of smoothing technique on twin support vector machines. Pattern Recogn Lett 29(13):1842–1848CrossRef Kumar MA, Gopal M (2008) Application of smoothing technique on twin support vector machines. Pattern Recogn Lett 29(13):1842–1848CrossRef
28.
Zurück zum Zitat Chen X, Yang J, Liang J, Ye Q (2012) Smooth twin support vector regression. Neural Comput Appl 21(3):505–513CrossRef Chen X, Yang J, Liang J, Ye Q (2012) Smooth twin support vector regression. Neural Comput Appl 21(3):505–513CrossRef
29.
Zurück zum Zitat Mangasarian OL, Musicant DR (2001) Lagrangian support vector machines. J Machine Learn Res 1:161–177MATHMathSciNet Mangasarian OL, Musicant DR (2001) Lagrangian support vector machines. J Machine Learn Res 1:161–177MATHMathSciNet
30.
Zurück zum Zitat Joachims T (2002) Learning to classify text using support vector machines: methods, theory and algorithms. In: The Kluwer international series in engineering and computer science. Springer, New York Joachims T (2002) Learning to classify text using support vector machines: methods, theory and algorithms. In: The Kluwer international series in engineering and computer science. Springer, New York
31.
Zurück zum Zitat Gan H, Sang N, Huang R, Tong X, Dan Z (2013) Using clustering analysis to improve semi-supervised classification. Neurocomputing 101:290–298CrossRef Gan H, Sang N, Huang R, Tong X, Dan Z (2013) Using clustering analysis to improve semi-supervised classification. Neurocomputing 101:290–298CrossRef
32.
Metadaten
Titel
Laplacian smooth twin support vector machine for semi-supervised classification
verfasst von
Wei-Jie Chen
Yuan-Hai Shao
Ning Hong
Publikationsdatum
01.06.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 3/2014
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-013-0183-3

Weitere Artikel der Ausgabe 3/2014

International Journal of Machine Learning and Cybernetics 3/2014 Zur Ausgabe