An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A well-conditioned estimator for large-dimensional covariance matrices
Journal of Multivariate Analysis
SIBGRAPI '05 Proceedings of the XVIII Brazilian Symposium on Computer Graphics and Image Processing
Efficient model selection for regularized linear discriminant analysis
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Distribution modeling and simulation of gene expression data
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Distribution modeling and simulation of gene expression data
Computational Statistics & Data Analysis
Projection-pursuit approach to robust linear discriminant analysis
Journal of Multivariate Analysis
Shrinkage-based regularization tests for high-dimensional data with application to gene set analysis
Computational Statistics & Data Analysis
Asymptotic expansion and estimation of EPMC for linear classification rules in high dimension
Journal of Multivariate Analysis
Estimating mutual information for feature selection in the presence of label noise
Computational Statistics & Data Analysis
Computers in Biology and Medicine
Hi-index | 0.03 |
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.