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In this paper we address the problem of multivariate outlier detection using the (unsupervised) self-organizing map (SOM) algorithm introduced by Kohonen. We examine a number of techniques, based on summary statistics and graphics derived from the trained SOM, and conclude that they work well in cooperation with each other. Useful tools include the median interneuron distance matrix and the projection...
Supervised Fisher Linear Discriminant Analysis (LDA) is a classical dimensionality reduction approach. LDA assumes each class has a Gaussian density and may suffer from the singularity problem when handling high-dimensional data. We in this work consider more general class densities and show that optimizing LDA criterion cannot always achieve maximum class discrimination with the geometrical interpretation...
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