ZHAO Yongping and SUN Jianguo, “Improved Signi¯cant Vector Learning forParsimonious Regression,” Chinese Journal of Electronics, vol. 19, no. 1, pp. 133-137, 2010,
Citation:
ZHAO Yongping and SUN Jianguo, “Improved Signi¯cant Vector Learning forParsimonious Regression,” Chinese Journal of Electronics, vol. 19, no. 1, pp. 133-137, 2010,
ZHAO Yongping and SUN Jianguo, “Improved Signi¯cant Vector Learning forParsimonious Regression,” Chinese Journal of Electronics, vol. 19, no. 1, pp. 133-137, 2010,
Citation:
ZHAO Yongping and SUN Jianguo, “Improved Signi¯cant Vector Learning forParsimonious Regression,” Chinese Journal of Electronics, vol. 19, no. 1, pp. 133-137, 2010,
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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