Abstract
Self-organizing continuously-overlapping map is shown to have ability to detect the first and second nonlinear principal components. This is an extended version of the self-organizing overlapping mapping. The model was applied to FFT data of sound, and some others. These data are characterized by a combination of two kinds of features, such as the pitch and the quality of tone. The model has two self-organizing layers. One layer extracts and maps continuously one feature, and the other layer does the same with respect to the other feature. The ability of generalization depending on data structure is demonstrated. Comparison to Kohonen's SOM is also discussed.
