| Summary: | The main purpose of discriminant analysis is to enable classification of new observations into one of g classes or
populations. Discriminant methods suffer when applied to high dimensional data because the sample covariance matrix is
singular. In this study, we propose two new discriminant methods for high dimensional data under the multivariate normal
population with a block diagonal covariance matrix structure. As the first method, we approximate the sample covariance matrix
as a singular matrix based on the idea of reducing the dimensionality of the observations to get a well-conditioned covariance
matrix. As the second method, we use a block diagonal sample covariance matrix instead. The performances of these two
methods are compared with some of the existing methods in a simulation study. The results show that both proposed methods
outperform other comparative methods in various situations. In addition, the two new proposed methods for discriminant analysis
are applied to a real dataset.
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