A variety of problems in geographical and satellite-based remote sensing signal processing, and in the area of "zero-error" pattern recognition deal with processing the information contained in the distances between the points in the geographical or feature space. In this paper we consider one such problem, namely, that of reconstructing the points in the geographical or feature space, when we are only given the approsimate distances between the points themselves. In particular, we are interested in the problem of reconstructing a map when the given data is the set of intercity road travel distances. Reported solution approaches primarily involve multi-dimensional scaling techniques. However, we propose a self-organizing method. New method is tested and compared with the Classical multi-dimensional scaling and ALSCAL on different data sets obtained from various countries.

Additional Metadata
Persistent URL dx.doi.org/10.1109/NNSP.2003.1318067
Conference 13th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2003
Citation
Altinel, I.K. (I. Kuhan), Aras, N. (Necati), & Oommen, J. (2003). A self-organizing method for map reconstruction. In Neural Networks for Signal Processing - Proceedings of the IEEE Workshop (pp. 677–687). doi:10.1109/NNSP.2003.1318067