Generalized discriminant analysis for tumor classification with gene expression data

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 2006 International Conference on Machine Learning and Cybernetics
Pages4322-4327
Volume2006
Publication statusPublished - 2006

Publication series

Name
Volume2006

Conference

Title2006 International Conference on Machine Learning and Cybernetics
PlaceChina
CityDalian
Period13 - 16 August 2006

Abstract

DNA microarray technology is the latest and the most advanced tool for parallel measuring of the activity and interactions of thousands of genes. The challenge is that the data dimension is large compared to the number of data points, which leads to small sample size (SSS) problem. Principal Component Analysis plus Linear Discriminant Analysis (PCA+LDA) is a well-known technique to cope with this problem, however, it cannot completely solve the SSS problem. In this paper we propose two novel discriminant techniques. Experimental results on gene expression data sets demonstrate that our methods have good discriminating power and outperform the direct linear discriminant analysis, moreover they are more stable than the PCA+LDA approach. © 2006 IEEE.

Research Area(s)

  • Classification, Linear discriminant analysis, Microarray data analysis, RBF kernel, Small sample size problem

Citation Format(s)

Generalized discriminant analysis for tumor classification with gene expression data. / Yang, Wen-Hui; Dai, Dao-Qing; Yan, Hong.

Proceedings of the 2006 International Conference on Machine Learning and Cybernetics. Vol. 2006 2006. p. 4322-4327 4028833.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review