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
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2015

01.08.2015

Scalable Semi-Supervised Classification via Neumann Series

verfasst von: Chen Gong, Keren Fu, Lei Zhou, Jie Yang, Xiangjian He

Erschienen in: Neural Processing Letters | Ausgabe 1/2015

Einloggen

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

search-config
loading …

Abstract

Traditional graph-based semi-supervised learning (GBSSL) algorithms usually scale badly due to the expensive computational burden. The main bottleneck is that they need to compute the inversion of a huge matrix. In order to alleviate this problem, this paper proposes Neumann series approximation (NSA) to explicitly approximate the inversion process required by conventional GBSSL methodologies, which makes them computationally tractable for relatively large datasets. It is proved that the deviation between the approximation and direct inversion is bounded. Using real-world datasets related to handwritten digit recognition, speech recognition and text classification, the experimental results reveal that NSA accelerates the speed significantly without decreasing too much precision. We also empirically show that NSA outperforms other scalable approaches such as Nyström method, Takahashi equation, Lanczos process based SVD and AnchorGraph regularization, in terms of both efficiency and accuracy.
Vorheriger Artikel Non-negative Matrix Factorization with Pairwise Constraints and Graph Laplacian
Nächster Artikel Homogenous Spiking Neural P Systems with Inhibitory Synapses

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
Fußnoten
1
Lanczos process based SVD is implemented by using PROPACK, which is available at http://​soi.​stanford.​edu/​~rmunk/​PROPACK/​.
 
Literatur
1.
Belkin M et al (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434MathSciNetMATH
2.
Campbell Y, Davis T (1995) Computing the sparse inverse subset: an inverse multifrontal approach. Technical report TR-95-021, University of Florida, Gainesville, FL
3.
Delalleau O, Bengio Y et al (2005) Efficient non-parametric function induction in semi-supervised learning. In: Proceedings of the 10th international workshop on artificial intelligence and statistics, p 12–19
4.
Fergus R, Kandola J et al (2009) Semi-supervised learning in gigantic image collections. In: Proceedings of the advances in neural information processing systems, Vancouver
5.
Fowlkes C et al (2004) Spectral grouping using the nyström method. Pattern Anal Mach Intell IEEE Trans 26(2):214–225CrossRefMATH
6.
Garcke J, Griebel M (2005) Semi-supervised learning with sparse grids. In: Proceedings of the international conference on machine learning, Bonn
7.
Golub G, Loan V (1996) Matrix computations. Johns Hopkins University Press, Baltimore
8.
Kawano S et al (2012) Semi-supervised logistic discrimination via graph-based regularization. Neural Process Lett 36(3):203–216CrossRef
9.
Larsen R (1998) Lanczos bidiagonalization with partial reorthogonalization. DAIMI Report Series 27(537):1–101
10.
Liu W, He J et al (2010) Large graph construction for scalable semi-supervised learning. In: Proceedings of the international conference on machine learning, Haifa, pp 679–686
11.
Salton G, McGill M (1986) Introduction to modern information retrieval, McGraw-Hill, New York
12.
Shang F et al (2012) Integrating spectral kernel learning and constraints in semi-supervised classification. Neural Process Lett 36(2):101–115CrossRef
13.
Sinha K, Belkin M (2009) Semi-supervised learning using sparse eigenfunction bases. Adv Neural Info Process Sys 22:1687–1695
14.
15.
Subramanya A, Bilmes J (2011) Semi-supervised learning with measure propagation. J Mach Learn Res 12:3311–3370MathSciNet
16.
Talwalkar A, Kumar S et al (2008) Large-scale manifold learning. In: Proceedings of the IEEE conference on the computer vision and pattern recognition (CVPR)
17.
Tsang I, Kwok J (2007) Large-scale sparsified manifold regularization. In: Proceedings of the advances in neural information processing systems, Vancouver
18.
Valls G et al (2007) Semi-supervised graph-based hyperspectral image classification. Geosci Remote Sens IEEE Trans 45(10):3044–3054CrossRef
19.
Wang J et al (2009) Linear neighborhood propagation and its applications. Pattern Anal Mach Intell IEEE Trans 31(9):1600–1615CrossRef
20.
Zhang K, Kwok J, et al (2009) Prototype vector machine for large scale semi-supervised learning. In: Proceedings of the international conference on machine learning, pp 1233–1240
21.
Zhou D, Bousquet O (2003) Learning with local and global consistency. In: Proceedings of the advances in neural information processing systems, Vancouver, pp 321–328
22.
Zhu X, Ghahramani Z et al (2003) Semi-supervised learning using Gaussian fields and harmonic functions. In: Proceedings of the international conference on machine learning, Washington, DC, pp 912–919
23.
Zhu X, Goldberg B (2009) Introduction to semi-supervised learning. Morgan & Claypool Publishers, San RafaelMATH
24.
Zhu X (2005) Lafferty: Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning. In: Proceedings of the international conference on machine learning, Bonn, pp 1052–1059
Metadaten
Titel
Scalable Semi-Supervised Classification via Neumann Series
verfasst von
Chen Gong
Keren Fu
Lei Zhou
Jie Yang
Xiangjian He
Publikationsdatum
01.08.2015
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2015
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-014-9351-z

Weitere Artikel der Ausgabe 1/2015

Neural Processing Letters 1/2015 Zur Ausgabe

OriginalPaper

Multi-sensor Fusion Based on Asymmetric Decision Weighting for Robust Activity Recognition

OriginalPaper

Neighborhood Guided Smoothed Emphasis for Real Adaboost Ensembles

OriginalPaper

Towards Robust Neural-Network-Based Sensor and Actuator Fault Diagnosis: Application to a Tunnel Furnace

OriginalPaper

Using Discriminative Dimensionality Reduction to Visualize Classifiers

OriginalPaper

An Agent-Based Model for Simulating Environmental Behavior in an Educational Organization

OriginalPaper

Homogenous Spiking Neural P Systems with Inhibitory Synapses

Neuer Inhalt

    Bildnachweise