Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies
This is an incremental improvement for researchers analyzing high-dimensional datasets like microarray studies, offering a method that combines classification and feature elimination.
The authors tackled the problem of high-dimensional data classification with feature selection by proposing compressive regularized discriminant analysis (CRDA), which resulted in fewer misclassification errors and accurate feature selection compared to competitors in simulations and real-life microarray datasets.
We propose a modification of linear discriminant analysis, referred to as compressive regularized discriminant analysis (CRDA), for analysis of high-dimensional datasets. CRDA is specially designed for feature elimination purpose and can be used as gene selection method in microarray studies. CRDA lends ideas from $\ell_{q,1}$ norm minimization algorithms in the multiple measurement vectors (MMV) model and utilizes joint-sparsity promoting hard thresholding for feature elimination. A regularization of the sample covariance matrix is also needed as we consider the challenging scenario where the number of features (variables) is comparable or exceeding the sample size of the training dataset. A simulation study and four examples of real-life microarray datasets evaluate the performances of CRDA based classifiers. Overall, the proposed method gives fewer misclassification errors than its competitors, while at the same time achieving accurate feature selection.