A supervised training algorithm for self-organizing maps for structures

Recent developments with self-organizing maps allow the application to graph structured data. This paper proposes a supervised learning technique for self-organizing maps for structured data. The ideas presented in this paper differ from Kohonen's approach in that a rejection term is introduced. This approach is superior because it is more robust to the variation of the number of different classes in a dataset. It is also more flexible because it is able to efficiently process data with missing or incomplete class information, and hence, includes the unsupervised version as a special case. We demonstrate the capabilities of the proposed model through an application to a relatively large practical data set from the area of image recognition, viz., logo recognition. It is shown that by adding supervised learning to the learning process the discrimination between pattern classes is enhanced, while the computational complexity is similar to that of the unsupervised version.