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IEEEMM
2007

Learning Microarray Gene Expression Data by Hybrid Discriminant Analysis

10 years 1 months ago
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 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 perf...
Yijuan Lu, Qi Tian, Maribel Sanchez, Jennifer L. N
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2007
Where IEEEMM
Authors Yijuan Lu, Qi Tian, Maribel Sanchez, Jennifer L. Neary, Feng Liu, Yufeng Wang
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