Learning Microarray Gene Expression Data by Hybrid Discriminant Analysis
Microarray technology offers a high-throughput means to study expression networks and gene regulatory networks in cells. The intrinsic nature of high dimensionality and small sample size in microarray data calls for effective computational methods. In this article, we propose a novel hybrid dimension reduction technique for classification that combines principal component analysis (PCA) and linear discriminant analysis (LDA)-hybrid PCA and LDA analysis. This technique effectively solves the singular scatter matrix problem caused by small training samples and increases the effective dimension of the projected subspace. It offers more flexibility and a richer set of alternatives to LDA and PCA in the parametric space. In addition, we propose a boosted hybrid discriminant analysis (HDA), using the AdaBoost algorithm which provides a unified and stable solution to find close to the optimal PCA-LDA prediction result and also reduces computational complexity. Extensive experiments on the yeast cell cycle regulation data set show the superior performance of the hybrid analysis, as we explain.