Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 5/2015

01.10.2015 | Original Article

Nonnegative matrix factorization with manifold regularization and maximum discriminant information

verfasst von: Wenjun Hu, Kup-Sze Choi, Jianwen Tao, Yunliang Jiang, Shitong Wang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 5/2015

Einloggen

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

search-config
loading …

Abstract

Nonnegative matrix factorization (NMF) has been successfully used in different applications including computer vision, pattern recognition and text mining. NMF aims to decompose a data matrix into the product of two matrices (respectively denoted as the basis vectors and the encoding vectors), whose entries are constrained to be nonnegative. Unlike the ordinary NMF, we propose a novel NMF, denoted as MMNMF, which considers both geometrical information and discriminative information hidden in the data. The geometrical information is discovered by minimizing the distance among the encoding vectors, while the discriminative information is uncovered by maximizing the distance among base vectors. Clustering experiments are performed on the real-world data sets of faces, images, and documents to demonstrate the effectiveness of the proposed algorithm.
Vorheriger Artikel Directivity and SLL optimization of time modulated concentric circular antenna array synthesis: an hybrid evolutionary approach
Nächster Artikel Methods of selection and identification of RFID tags

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Weitere Produktempfehlungen anzeigen
Fußnoten
Literatur
1.
Paatero P, Tapper U (1994) Positive matrix factorization—a nonnegative factor model with optimal utilization of error estimates of data values. Environmetrics 5(2):111–126CrossRef
2.
Lee DD, Seung HS (1999) Learning the parts of objects by nonnegative matrix factorization. Nature 401:788–791CrossRef
3.
Hyvärinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Networks 13(4–5):411–430CrossRef
4.
Hyvärinen A, Karhunen J, Oja E (2001) Independent component analysis. Wiley, New YorkCrossRef
5.
Xu W, Liu X, Gong Y (2003) Document clustering based on non-negative matrix factorization. Proc. ACM Conf. Research and Development in Information Retrieval (SIGIR), pp 267–273
6.
Pauca VP, Shahnaz F, Berry M, Plemmons R (2004) Text mining using non-negative matrix factorization. Proc. SIAM Int’l Conf. Data Mining, pp 452–456
7.
Li S, Hou X, Zhang H, Cheng Q (2001) Learning spatially localized, parts-based representation. Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp 207–212
8.
Shahnaza F, Berrya MW, Paucab V, Plemmonsb RJ (2006) Document clustering using nonnegative matrix factorization. Inf Process Manage 42(2):373–386CrossRef
9.
Pascual-Montano A, Carazo JM, Kochi K, Lehmann D, Pascual-Marqui RD (2006) Nonsmooth nonnegative matrix factorization (nsNMF). IEEE Trans Pattern Anal Mach Intell 28(3):403–415CrossRef
10.
Cai D, He X, Han J, Huang T (2011) Graph regularized non-negative matrix factorization for data representation. IEEE Trans Pattern Anal Mach Intell 33(8):1548–1560CrossRef
11.
Ding C, Li T, Jordan MI (2010) Convex and semi-nonnegative matrix factorizations. IEEE Trans Pattern Anal Mach Intell 32(1):45–55CrossRef
12.
Guan NY, Tao DC, Luo ZG, Yuan B (2011) Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent. IEEE Trans Image Process 20(7):2030–2048MathSciNetCrossRef
13.
Cui JR, Wen JJ, Li ZM, Li B (2015) Discriminant non-negative graph embedding for face recognition. Neurocomputing 149:1451–1460CrossRef
14.
Wang Jim JY, Bensmail H, Gao X (2013) Multiple graph regularized nonnegative matrix factorization”. Pattern Recogn 46(10):2840–2847CrossRef
15.
Yang S, Yi Z, Ye M, He X (2014) Convergence analysis of graph regularized non-negative matrix factorization. IEEE Trans Knowl Data Eng 26(9):2151–2155CrossRef
16.
Fisher RA (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics 7(2):179–188CrossRef
17.
Rao CR (1948) The utilization of multiple measurements in problems of biological classification, J R Statis Soci Series B 10(2):159–203MATH
18.
Lee DD, Seung HS (2001) Algorithms for non-negative matrix factorization,Advances in Neural Information Processing Systems 13, MIT Press, USA
19.
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, pp 281–297
20.
He X, Niyogi P (2003) Locality preserving projections. Proceeding in Conference Advances in Neural Information Processing Systems (NIPS)
21.
M. Belkin, P. Niyogi, “Laplacian eigenmaps and spectral techniques for embedding and clustering,” Advances in Neural Information Processing Systems 14, pp. 585-591, MIT Press, 2001
22.
Fan R. K. Chung, Spectral Graph Theory, Regional Conference Series in Mathematics, no. 92, 1997
23.
He X, Cai D, Yan S, Zhang H (2005) Neighborhood preserving embedding. Proceeding in International Conference on Computer Vision (ICCV)
24.
Lin CJ (2007) On the convergence of multiplicative update algorithms for non-negative matrix factorization. IEEE Trans Neural Networks 18(6):1589–1596CrossRef
25.
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905CrossRef
26.
Wu Z, Leahy R (1993) An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Trans Pattern Anal Mach Intell 15(11):1101–1113CrossRef
27.
Lovasz L, Plummer M (1986) Matching Theory. Akadémiai Kiadó, North HollandMATH
28.
Cai D, He X, Han J (2005) Document clustering using locality preserving indexing. IEEE Trans Knowl Data Eng 17(12):1624–1637CrossRef
29.
Wang XZ, Xing HJ, LI Y, Hua Q et al (2014) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst. doi:10.​1109/​TFUZZ.​2014.​2371479
30.
Wang XZ, Dong LC, Yan JH (2012) Maximum ambiguity-based sample selection in fuzzy decision tree induction. IEEE Trans Knowl Data Eng 24(8):1491–1505CrossRef
31.
Wang XZ, Dong CR (2009) Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy. IEEE Trans Fuzzy Syst 17(3):556–567CrossRef
Metadaten
Titel
Nonnegative matrix factorization with manifold regularization and maximum discriminant information
verfasst von
Wenjun Hu
Kup-Sze Choi
Jianwen Tao
Yunliang Jiang
Shitong Wang
Publikationsdatum
01.10.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 5/2015
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0396-8

Weitere Artikel der Ausgabe 5/2015

International Journal of Machine Learning and Cybernetics 5/2015 Zur Ausgabe

Original Article

Methods of selection and identification of RFID tags

Original Article

Iterative search method for total flowtime minimization no-wait flowshop problem

Original Article

Towards an optimally pruned classifier ensemble

Original Article

Enhancing the management of shared inventory in the steel industry using RFID: an alternative to bar codes

Original Article

Directivity and SLL optimization of time modulated concentric circular antenna array synthesis: an hybrid evolutionary approach

Original Article

Gujarati character recognition using adaptive neuro fuzzy classifier with fuzzy hedges

Neuer Inhalt

    Bildnachweise