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Fast multi-output relevance vector regression

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  • Ha, Youngmin
  • Zhang, Hai

Abstract

This paper has applied the matrix Gaussian distribution of the likelihood function of the complete data set to reduce time complexity of multi-output relevance vector regression from OVM3 to OV3+M3, where V and M are the number of output dimensions and basis functions respectively and V < M. Our experimental results demonstrate that the proposed method is more competitive and faster than the existing methods like Thayananthan et al. (2008). Its computational efficiency and accuracy can be attributed to the different model specifications of the likelihood of the data, as the existing method expresses the likelihood of the training data as the product of Gaussian distributions whereas the proposed method expresses it as the matrix Gaussian distribution.

Suggested Citation

  • Ha, Youngmin & Zhang, Hai, 2019. "Fast multi-output relevance vector regression," Economic Modelling, Elsevier, vol. 81(C), pages 217-230.
    • Handle: RePEc:eee:ecmode:v:81:y:2019:i:c:p:217-230
    DOI: 10.1016/j.econmod.2019.04.007
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    References listed on IDEAS

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    1. Tao Xiong & Yukun Bao & Zhongyi Hu, 2014. "Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting," Papers 1401.1916, arXiv.org.
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    1. Kai Biehl & Franziska Disslbacher & Michael Ertl & Georg Feigl & Julia Hofmann & Pia Kranawetter & Markus Marterbauer & Michael Mesch & Reinhold Russinger & Matthias Schnetzer & Tobias Schweitzer & Th, 2019. "Neue Legislaturperiode: Weichen für wohlstandsorientierte Budgetpolitik stellen," Wirtschaft und Gesellschaft - WuG, Kammer für Arbeiter und Angestellte für Wien, Abteilung Wirtschaftswissenschaft und Statistik, vol. 45(4), pages 459-470.

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