Learning Microarray Gene Expression Data by Hybrid Discriminant Analysis




Lu, Yijuan
Tian, Qi
Sanchez, Maribel
Neary, Jennifer
Liu, Feng
Wang, Yufeng

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UTSA Department of Computer Science


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 paper, we propose a novel hybrid dimension reduction technique for classification - hybrid PCA (principal component analysis) and LDA (linear discriminant analysis) 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, a boosted hybrid discriminant analysis is also proposed, which provides a unified and stable solution to find close to the optimal PCA-LDA prediction result and reduces computational complexity. Extensive experiments on the yeast cell cycle regulation data set show the superior performance of the hybrid analysis.



LDA, PCA, dimension reduction, microarray analysis



Computer Science