A graph-Laplacian PCA based on L1/2-norm constraint for characteristic gene selection

Principal Component Analysis (PCA) as a tool for dimensionality reduction is widely used in many areas. In the area of bioinformatics, the first principal component of PCA is used to select characteristic genes. In order to improve the robustness of PCA-based method, this paper proposes a novel graph-Laplacian PCA algorithm by adopting L1/2 constraint on error function (L1/2 gLPCA) for characteristic gene selection. Augmented Lagrange Multipliers (ALM) method is applied to solve the sub-problem. This method gets better results in characteristic gene selection than traditional PCA approach. Meanwhile, the error function based on the L1/2 norm helps to reduce the influence of outliers and noise. Extensive experimental results on gene expression data sets demonstrate that our method can get higher identification accuracies than others.