Fast multi-output relevance vector regression
Ha, Youngmin and Zhang, Hai (2019) Fast multi-output relevance vector regression. Economic Modelling, 81. pp. 217-230. ISSN 0264-9993 (https://doi.org/10.1016/j.econmod.2019.04.007)
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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 O(VM^3) to O(V^3 +M^3), 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.
ORCID iDs
Ha, Youngmin and Zhang, Hai ORCID: https://orcid.org/0000-0001-9319-346X;-
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Item type: Article ID code: 67539 Dates: DateEvent1 September 2019Published20 April 2019Published Online9 April 2019AcceptedSubjects: Social Sciences > Economic Theory Department: Strathclyde Business School > Accounting and Finance Depositing user: Pure Administrator Date deposited: 10 Apr 2019 08:58 Last modified: 01 Oct 2024 08:53 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/67539