Abstract
We propose a simple affine equivariant clustering method, based on the idea of best linear classification, for samples from a mixture of two multivariate normal distributions with different mean vectors but proportional covariance matrices. To ameliorate the curse of dimensionality, a non-parametric approach to find candidates for a best linear discriminant function is presented. By using simulation studies and a real example, we show that for large samples in high dimensions, the proposed method can be a useful supplement to general-purpose multivariate outlier detection methods.
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