 Modified linear discriminant analysis approaches for classification of high-dimensional microarray dataPing Xu, Guy N. Brock and Rudolph S. Parrish Computational Statistics & Data Analysis, 2009, vol. 53, issue 5, 1674-1687 Abstract: Linear discriminant analysis (LDA) is one of the most popular methods of classification. For high-dimensional microarray data classification, due to the small number of samples and large number of features, classical LDA has sub-optimal performance corresponding to the singularity and instability of the within-group covariance matrix. Two modified LDA approaches (MLDA and NLDA) were applied for microarray classification and their performance criteria were compared with other popular classification algorithms across a range of feature set sizes (number of genes) using both simulated and real datasets. The results showed that the overall performance of the two modified LDA approaches was as competitive as support vector machines and other regularized LDA approaches and better than diagonal linear discriminant analysis, k-nearest neighbor, and classical LDA. It was concluded that the modified LDA approaches can be used as an effective classification tool in limited sample size and high-dimensional microarray classification problems. Date: 2009 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:53:y:2009:i:5:p:1674-1687 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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