  We propose a compressive classification framework for settings where the data
dimensionality is significantly higher than the sample size. The proposed
method, referred to as compressive regularized discriminant analysis (CRDA) is
based on linear discriminant analysis and has the ability to select significant
features by using joint-sparsity promoting hard thresholding in the
discriminant rule. Since the number of features is larger than the sample size,
the method also uses state-of-the-art regularized sample covariance matrix
estimators. Several analysis examples on real data sets, including image,
speech signal and gene expression data illustrate the promising improvements
offered by the proposed CRDA classifier in practise. Overall, the proposed
method gives fewer misclassification errors than its competitors, while at the
same time achieving accurate feature selection results. The open-source R
package and MATLAB toolbox of the proposed method (named compressiveRDA) is
freely available.
