Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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SVQR with asymmetric quadratic loss function

  • Shim, Jooyong (Department of Statistics, Inje University) ;
  • Kim, Malsuk (Department of Social Welfare & Childcare, Yeungnam University College) ;
  • Seok, Kyungha (Department of Statistics, Inje University)
  • Received : 2015.08.18
  • Accepted : 2015.11.23
  • Published : 2015.11.30
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Abstract

Support vector quantile regression (SVQR) can be obtained by applying support vector machine with a check function instead of an e-insensitive loss function into the quantile regression, which still requires to solve a quadratic program (QP) problem which is time and memory expensive. In this paper we propose an SVQR whose objective function is composed of an asymmetric quadratic loss function. The proposed method overcomes the weak point of the SVQR with the check function. We use the iterative procedure to solve the objective problem. Furthermore, we introduce the generalized cross validation function to select the hyper-parameters which affect the performance of SVQR. Experimental results are then presented, which illustrate the performance of proposed SVQR.

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References

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