Self-organizing maps (SOMs) introduced by Kohonen (Biol. Cybern. 43(1): [59][60][61][62][63][64][65][66][67][68][69] 1982) are well-known in the field of artificial neural networks. The way SOMs are performing is very intuitive, leading to great popularity and numerous applications (related to statistics: classification, clustering). The result of the unsupervised learning process performed by SOMs is a non-linear, low-dimensional projection of the high-dimensional input data, that preserves certain features of the underlying data, e.g. the topology and probability distribution (Lee and Verleysen in Nonlinear Dimensionality Reduction, Springer, 2007; Kohonen in Selforganizing Maps, 3rd edn., Springer, 2001).With the U-matrix Ultsch (Information and Classification: Concepts, Methods and Applications, pp. 307-313, Springer, 1993) introduced a powerful visual representation of the SOM results. We propose an approach that utilizes the U-matrix to identify outlying data points. Then the revised subsample (i.e. the initial sample minus the outlying points) is used to give a robust estimation of location and scatter.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.