Self-Organizing Feature-Mapping (SOFM) algorithm is frequently used for visualization of high-dimensional (input) data in a lower-dimensional (target) space. This algorithm is based on adaptation of parameters in local neighborhoods and therefore does not lead to the best global visualization of the input space data clusters. SOFM is compared here with alternative methods of global visualization of multidimensional data, such as the multidimensional scaling (MDS) and Sammon non-linear maping, methods based on minimization of error function measuring topographical distoritions. SOFM is inferior as a visualization method but facilitates faster classification. A combination of global methods with SOFM should be useful for visualization and classification.
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