Nonlinear feature extraction with radial basis functions using a weighted multidimensional scaling stress measure
- 1 January 1996
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 4, 635-639 vol.4
- https://doi.org/10.1109/icpr.1996.547642
Abstract
We investigate radial basis functions for nonlinear feature extraction. The parameters of the transformation are determined by minimising a loss term (similar to stress in multidimensional scaling) that weights components of the loss by a nonlinear function of the dissimilarities. Several forms for the nonlinear function are considered and an optimisation scheme based on iterative majorisation is used to determine the parameter values. The technique is illustrated on two data sets.Keywords
This publication has 8 references indexed in Scilit:
- An approach to non-linear principal components analysis using radially symmetric kernel functionsStatistics and Computing, 1996
- Adaptive radial basis functionsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1996
- Multidimensional scaling by iterative majorization using radial basis functionsPattern Recognition, 1995
- Evaluation of pattern classifiers for fingerprint and OCR applicationsPattern Recognition, 1994
- Radial basis function neural network for direction-of-arrivals estimationIEEE Signal Processing Letters, 1994
- A Handbook of Small Data SetsPublished by Springer Nature ,1994
- Determination of anaerobic thresholdThe Canadian Journal of Statistics / La Revue Canadienne de Statistique, 1988
- A Nonlinear Mapping for Data Structure AnalysisIEEE Transactions on Computers, 1969