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Self-Organizing Maps

  • Ron WehrensEmail author
Chapter
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Part of the Use R book series (USE R)

Abstract

In PCA, the most outlying data points determine the direction of the PCs – these are the ones contributing most to the variance. This often results in score plots showing a large group of points close to the centre. As a result, any local structure is hard to recognize, even when zooming in: such points are not important in the determination of the PCs. One approach is to select the rows of the data matrix corresponding to these points, and to perform a separate PCA on them. Apart from the obvious dificulties in deciding which points to leave out and which to include, this leads to a cumbersome and hard to interpret two-step approach. It would be better if a projection can be found that does show structure, even within very similar groups of points.

Keywords

Outlying Data Point Batch Algorithm Codebook Vector Training Progress Sammon Mapping 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Research and Innovation CentreFondazione Edmund MachSan Michele all’AdigeItaly

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