NDLI: Learning Microarray Gene Expression Data by Hybrid Discriminant Analysis

Content Provider	IEEE Xplore Digital Library
Author	Yijuan Lu Qi Tian Sanchez, M. Neary, J. Feng Liu Yufeng Wang
Copyright Year	1994
Abstract	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.
Sponsorship	IEEE Computer Society
File Size	747359
File Format	PDF
ISSN	1070986X
Volume Number	14
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gene expression Linear discriminant analysis Principal component analysis Data analysis Classification tree analysis Computational complexity Testing Algorithm design and analysis Pattern analysis Signal processing algorithms and microarray analysis. LDA PCA dimension reduction
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Science Applications Software Hardware and Architecture Media Technology

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