Computer Science ›› 2017, Vol. 44 ›› Issue (1): 243-246, 282.doi: 10.11896/j.issn.1002-137X.2017.01.045

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Sparse Controllable Principal Component Analysis Method

TAN Ya-fang, LIU Juan, WANG Cai-hua and JIANG Wan-wei   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Principal component analysis (PCA) is a multivariate statistical analysis method which chooses a few important variables (dimension reduction) by linear transformation.PCA is widely used in scientific researches and enginee-ring,however,the results can sometimes be difficult to interpret.Therefore,some researchers introduced sparse penalties (lasso,fused lasso and adaptive lasso etc.) to obtain interpretable results.Since the traditional sparse penalty is not easy to control,we presented a novel penalty,namely sparse controllable penalty (SCP),to control the sparsity of principal components.Compared with the traditional penalties,SCP is scale insensitive,dimension insensitive and bounded between 0 and 1.It is easy to adjust the super parameter to control sparseness.Experimental results demonstrate that sparse controllable principal component analysis (SCPCA) is efficient.

Key words: Principal component analysis,Sparse penalty,Sparse controllable principal component analysis

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