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A Fast-Converging Algorithm for Nonlinear Mapping of High-Dimensional Data to a Plane
February 1979 (vol. 28 no. 2)
pp. 142-147
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.
Index Terms:
unsupervised learning, Cluster analysis, coordinate descent, dimensionality reduction, iterative algorithm, nonlinear mapping, steepest descent
Citation:
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, Feb. 1979, doi:10.1109/TC.1979.1675303
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