Issue No.02 - February (1979 vol.28)
H. Niemann , Friedrich-Alexander-Universitat, Institut fur Mathematische Maschinen und Datenverarbeitung
An iterative algorithm for nonlinear mapping of high-dimensional data is developed. The step size of the descent algorithm is chosen to assure convergence. Steepest descent and Coordinate descent are treated. The algorithm is applied to artificial and real data to demonstrate its excellent convergence properties.
unsupervised learning, Cluster analysis, coordinate descent, dimensionality reduction, iterative algorithm, nonlinear mapping, steepest descent
H. Niemann, J. Weiss, "A Fast-Converging Algorithm for Nonlinear Mapping of High-Dimensional Data to a Plane", IEEE Transactions on Computers, vol.28, no. 2, pp. 142-147, February 1979, doi:10.1109/TC.1979.1675303