Non-Euclidean self-organizing classification using natural manifold distance

Current unsupervised classification using self organizing mapping (SOM) competitive learning is based on the minimum Euclidean distance between a prototype neuron and the selected data. This is not suitable for several classification problems where the geometrical structure and curvature of the data space are the main concern. The problem studied in This work concerns the algorithm for measuring the non-Euclidean distance in a data point space, i.e. the surface function is unknown, and moving the prototype neurons along the actual geometrical structure of the data points. Our algorithm successfully classifies the experimental data spaces with various aspects while the SOM classification gives incorrect results.