ZHAO Yongping and SUN Jianguo. Improved Signi¯cant Vector Learning forParsimonious Regression[J]. Chinese Journal of Electronics, 2010, 19(1): 133-137.
Citation: ZHAO Yongping and SUN Jianguo. Improved Signi¯cant Vector Learning forParsimonious Regression[J]. Chinese Journal of Electronics, 2010, 19(1): 133-137.

Improved Signi¯cant Vector Learning forParsimonious Regression

  • Received Date: 1900-01-01
  • Rev Recd Date: 1900-01-01
  • Publish Date: 2010-01-05
  • Although the Signi¯cant vector (SV) re-
    gression algorithm was proposed for constructing parsimo-
    nious regression model, yet it can only ¯nd a suboptimal
    solution. Hence, in this paper, an improved scheme is pro-
    posed to boost the performance of the SV algorithm. The
    Improved signi¯cant vector (ISV) algorithm without the
    complicated regularization technique achieves as excellent
    results as the Regularized SV algorithm (RSV), meantime
    with less number of regressors. In addition, compared
    with other algorithms, i.e., local regularization assisted or-
    thogonal least squares (LROLS), Relevance vector machine
    (RVM), and the Modi¯ed Gram-Schmidt (MGS), the ISV
    algorithm is also favorable in the parsimoniousness. Fi-
    nally, simulation examples validate the e®ectiveness and
    feasibility of the ISV algorithm.
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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