Learning Microarray Gene Expression Data by Hybrid Discriminant Analysis

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dc.contributor.authorLu, Yijuan
dc.contributor.authorTian, Qi
dc.contributor.authorSanchez, Maribel
dc.contributor.authorNeary, Jennifer
dc.contributor.authorLiu, Feng
dc.contributor.authorWang, Yufeng
dc.date.accessioned2023-10-24T14:38:09Z
dc.date.available2023-10-24T14:38:09Z
dc.date.issued2007-12
dc.description.abstractMicroarray 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.
dc.description.departmentComputer Science
dc.description.sponsorshipThis work is supported in part by San Antonio Life Science Institute (SALSI) to Q. Tian and F. Liu, ARO grant W911NF-05-1-0404 to Q. Tian, and San Antonio Area Foundation Biomedical Research Funds, NIH RCMI grant 2G12RR013646-06A1, and UTSA Faculty Research Awards to Y. Wang. J. Neary is supported by NIH MBRS-RISE (Minority Biomedical Research Support Research Initiative for Scientific Enhancement - grant GM-60655).
dc.identifier.urihttps://hdl.handle.net/20.500.12588/2134
dc.language.isoen_US
dc.publisherUTSA Department of Computer Science
dc.relation.ispartofseriesTechnical Report; CS-TR-2007-014
dc.subjectLDA
dc.subjectPCA
dc.subjectdimension reduction
dc.subjectmicroarray analysis
dc.titleLearning Microarray Gene Expression Data by Hybrid Discriminant Analysis
dc.typeTechnical Report

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