J4 ›› 2012, Vol. 47 ›› Issue (4): 116-120.

• Articles • Previous Articles     Next Articles

Sparse principal component analysis for symmetric matrix and  application in sufficient dimension reduction

SHAO Wei1, ZHU Li-ping2, LIU Fu-Guo2, WANG Qiu-Ping2   

  1. 1. School of Mathimatics, Shandong University, Jinan 250100, Shandong, China;
    2. Department of Mathematics, Changji College, Changji 831100, Xinjiang, China
  • Received:2011-05-16 Online:2012-04-20 Published:2012-06-28

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Abstract:

 Sparse principal component analysis(SPC) for symmetric matrix and application are discussed. Asymptotic properties are obtained. Monte Carlo based simulations are used to illustrate the efficacy of the new method.

Key words: symmetric matrix; principal component analysis; sparse principal component analysis; sufficient dimension reduction; Monte Carlo; LASSO

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