Using uncorrelated discriminant analysis for tissue classification with gene expression data

Jieping Ye, Tao Li, Tao Xiong, Ravi Janardan

Research output: Contribution to journalArticlepeer-review

161 Scopus citations


The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes Is usually very high (in the thousands) compared to the number of data samples (in the tens or low hundreds); that is, the data dimension is large compared to the number of data points (such data is said to be undersampled). To cope with performance and accuracy problems associated with high dimensionality, it is commonplace to apply a preprocessing step that transforms the data to a space of significantly lower dimension with limited loss of the information present in the original data. Linear Discriminant Analysis (LDA) is a well-known technique for dimension reduction and feature extraction, but it is not applicable for undersampled data due to singularity problems associated with the matrices in the underlying representation. This paper presents a dimension reduction and feature extraction scheme, called Uncorrelated Linear Discriminant Analysis (ULDA), for undersampled problems and illustrates its utility on gene expression data. ULDA employs the Generalized Singular Value Decomposition method to handle undersampled data and the features that it produces in the transformed space are uncorrelated, which makes it attractive for gene expression data. The properties of ULDA are established rigorously and extensive experimental results on gene expression data are presented to illustrate its effectiveness in classifying tissue samples. These results provide a comparative study of various state-of-the-art classification methods on well-known gene expression data sets.

Original languageEnglish (US)
Pages (from-to)181-190
Number of pages10
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number4
StatePublished - Oct 2004

Bibliographical note

Funding Information:
The authors would like to thank the associate editor and the reviewers for helpful comments that greatly improved the paper. This research is sponsored, in part, by the Army High Performance Computing Research Center under the auspices of the Department of the Army, Army Research Laboratory cooperative agreement number DAAD19-01-2-0014, the content of which does not necessarily reflect the position or the policy of the government, and no official endorsement should be inferred. The research of J. Ye is also supported by fellowships from Guidant Corporation and from the Department of Computer Science and Engineering at the University of Minnesota.


  • Classification
  • Discriminant analysis
  • Generalized singular value decomposition
  • Microarray data analysis


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