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Volume 26 Issue 4
Apr.  2000
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Journal of Beijing University of Aeronautics and Astronautics  > 2000  >  26(4): 473-476.
Chen Gang, Zhang Jinbai, Lee Chun-Hianet al. Numerical simulation of external MHD generator on board reentry vehicles[J]. Journal of Beijing University of Aeronautics and Astronautics, 2010, 36(2): 135-139. (in Chinese)
Citation: WANG Hui-ping, LIU Qiang, TU Yong-pinget al. Identification of Optimal Subspace from PLS Regression[J]. Journal of Beijing University of Aeronautics and Astronautics, 2000, 26(4): 473-476. (in Chinese)
shu

Identification of Optimal Subspace from PLS Regression

  • 1.

    Beijing University of Aeronautics and Astronautics,School of Management

  • 2. Beijing University of Aeronautics and Astronautics,School of Mechanical Engineering and Automation

  • Received Date: 23 Mar 1999
  • Publish Date: 30 Apr 2000
    Abstract
  • Partial least-squares regression, a novel approach for multivariate analysis, is widely used for modeling a multi-collinear variable data set, with improved model accuracy and reliability based on building a subspace with most explanatory ability to the data set. In this paper the factor analysis method is presented to transform orthogonally the optimal subspace, which is obtained from partial least-squares regression. The transformation can identify each factor in a meaningful way but does not change the results of partial least-squares regression model. Therefore, the physical meaning of the optimal subspace of partial least-squares regression can be illustrated. A case study demonstrates that the original variable set is divided into several variable groups after the orthogonal transform, each of which is corresponding to a new factor in the subspace such that its explanatory ability is improved.

     

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    • References(1)
  • [1] Tenenhaus M. La regression PLS theorie et pratique[M]. Paris:Editions Technip, 1998. [2]Wold S, Ruhe A, Wold H, et al. The collinearity problem in linear regression, The partial least squares (PLS) approach to generalized inerses[J]. SIAM J Science Statistics Computer, 1984,5(2):735~743. [3]Wold H. Partial least squares, in encyclopedis of statistical sciences[M]. New York:John Wiley & Ston, 1985, 581~591 [4]王惠文. 偏最小二乘回归方法以及应用[M]. 北京:国防工业出版社,1999. [5]Tenenhaus M, Gauchi J P, Menardo C. Regression PLS et applications[J]. Revue de Statistique Appliquee, 1995,53(1):7~63. [6]王 权. 现代因素分析[M]. 杭州:杭州大学出版社,1992.
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