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
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 2/2014

01.05.2014 | Regular Paper

Sparse regression mixture modeling with the multi-kernel relevance vector machine

verfasst von: Konstantinos Blekas, Aristidis Likas

Erschienen in: Knowledge and Information Systems | Ausgabe 2/2014

Einloggen

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

search-config
loading …

Abstract

A regression mixture model is proposed where each mixture component is a multi-kernel version of the Relevance Vector Machine (RVM). This mixture model exploits the enhanced modeling capability of RVMs, due to their embedded sparsity enforcing properties. In order to deal with the selection problem of kernel parameters, a weighted multi-kernel scheme is employed, where the weights are estimated during training. The mixture model is trained using the maximum a posteriori approach, where the Expectation Maximization (EM) algorithm is applied offering closed form update equations for the model parameters. Moreover, an incremental learning methodology is also presented that tackles the parameter initialization problem of the EM algorithm along with a BIC-based model selection methodology to estimate the proper number of mixture components. We provide comparative experimental results using various artificial and real benchmark datasets that empirically illustrate the efficiency of the proposed mixture model.
Nächster Artikel Belief propagation algorithms for finding the probable configurations over factor graph models

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 "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

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
Literatur
1.
Alon J, Sclaroff S, Kollios G, Pavlovic V (2003) Discovering clusters in motion time-series data. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), pp 375–381
2.
Antonini G, Thiran J (2006) Counting pedestrians in video sequences using trajectory clustering. IEEE Trans Circuits Syst Video Technol 16(8):1008–1020CrossRef
3.
Bishop C (2006) Pattern recognition and machine learning. Springer, BerlinMATH
4.
Blekas K, Likas A (2012) The mixture of multi-kernel relevance vector machines model. In: International Conference on Data Mining (ICDM), pp 111–120
5.
Blekas K, Nikou C, Galatsanos N, Tsekos NV (2008) A regression mixture model with spatial constraints for clustering spatiotemporal data. Int J Artif Intell Tools 17(5):1023–1041CrossRef
6.
Chudova D, Gaffney S, Mjolsness E, Smyth P (2003) Mixture models for translation-invariant clustering of sets of multi-dimensional curves. In: Proceedings of the Ninth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, Washington, pp 79–88
7.
Dempster A, Laird N, Rubin D (1977) Maximum likelihood from incomplete data via the EM algorithm. J Royal Stat Soc B 39:1–38MATHMathSciNet
8.
9.
Ding H, Trajcevski G, Scheuermann P, Wang X, Keogh E (2008) Querying and mining of time series data: experimental comparison of representations and distance measures. In PVLDB, pp 1542–1552
10.
Fraley C, Raftery AE (1998) Bayesian regularization for normal mixture estimation and model-based clustering. Comput J 41:578–588CrossRefMATH
11.
Gaffney S, Smyth P (2003) Curve clustering with random effects regression mixtures. In: Bishop CM, Frey BJ (eds) Proceedings of the ninth international workshop on artificial intelligence and statistics
12.
Girolami M, Rogers S (2005) Hierarchic bayesian models for kernel learning. In: International conference on machine learning (ICML’05), pp 241–248
13.
Gonen M, Alpaydin E (2011) Multiple kernel learning algorithms. J Mach Learn Res 12:2211–2268MathSciNet
14.
Gunn S, Kandola J (2002) Structural modelling with sparse kernels. Mach Learn 48:137–163CrossRefMATH
15.
Harrell F (2001) Regression modeling strategies. With applications to linear models, logistic regression and survival analysis. Springer, New YorkMATH
16.
Hu M, Chen Y, Kwok J (2009) Building sparse multiple-kernel SVM classifiers. IEEE Trans. Neural Netw 20(5):827–839CrossRef
17.
Keogh E, Lin J, Truppel W (2005) Clustering of time series subsequences is meaningless: implications for past and future research. Knowl Inf Syst KAIS 2:154–177CrossRef
18.
19.
Li J, Barron A (2000) Mixture density estimation. In: Advances in neural information processing systems, Vol 12. The MIT Press, Cambridge, pp 279–285
20.
21.
22.
23.
Pelekis N, Kopanakis I, Kotsifakos E, Frentzos E, Theodoridis Y (2011) Clustering uncertain trajectories. Knowl Inf Syst KAIS 28:117–147CrossRef
24.
Rakthanmanon T, Campana B et al (2013) Addressing big data time series: mining trillions of time series subsequences under dynamic time warping. ACM Trans Knowl Discov Data 7(3):1–31
25.
Schmolck A, Everson R (2007) Smooth relevance vector machine: a smoothness prior extension of the RVM. Mach Learn 68(2):107–135CrossRef
26.
27.
Seeger M (2008) Bayesian inference and optimal design for the sparse linear model. J Mach Learn Res 9:759–813MATHMathSciNet
28.
Shi J, Wang B (2008) Curve prediction and clustering with mixtures of Gaussian process functional regression models. Stat Comput 18:267–283CrossRefMathSciNet
29.
Smyth P (1997) Clustering sequences with hidden Markov models. In: Advances in neural information processing systems, pp 648–654
30.
Tipping M (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244MATHMathSciNet
31.
Ueda N, Nakano R, Ghahramani Z, Hinton G (2000) SMEM algorithm for mixture models. Neural Comput 12(9):2109–2128CrossRef
32.
Vlassis N, Likas A (2001) A greedy EM algorithm for Gaussian mixture learning. Neural Process Lett 15:77–87CrossRef
33.
34.
Williams B, Toussaint M, Storkey A (2008) Modelling motion primitives and their timing in biologically executed movements. In: Advances in neural information processing systems, vol 15, pp 1547–1554
35.
Williams O, Blake A, Cipolla R (2005) Sparse Bayesian learning for efficient visual tracking. IEEE Trans. Pattern Anal Mach Intell 27(8):1292–1304CrossRef
36.
Xiong Y, Yeung D-Y (2002) Mixtures of ARMA models for model-based time series clustering. In: IEEE international conference on data mining (ICDM), pp 717–720
37.
Zhong M (2006) A variational method for learning sparse Bayesian regression. Neurocomputing 69:2351–2355CrossRef
Metadaten
Titel
Sparse regression mixture modeling with the multi-kernel relevance vector machine
verfasst von
Konstantinos Blekas
Aristidis Likas
Publikationsdatum
01.05.2014
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 2/2014
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-013-0704-0

Weitere Artikel der Ausgabe 2/2014

Knowledge and Information Systems 2/2014 Zur Ausgabe

Regular Paper

Peer-to-peer information retrieval using shared-content clustering

Regular Paper

Analyzing topics and authors in chat logs for crime investigation

Regular Paper

Robust sparsity-preserved learning with application to image visualization

Regular Paper

Combining compound and single terms under language model framework

Regular Paper

Learning and clustering of fuzzy cognitive maps for travel behaviour analysis

Regular Paper

An efficient orientation distance–based discriminative feature extraction method for multi-classification

Premium Partner

    Bildnachweise