A Comparison of Projection Pursuit and Neural Network Regression Modeling
Abstract
Two projection based feedforward network learning methods for model(cid:173) free regression problems are studied and compared in this paper: one is the popular back-propagation learning (BPL); the other is the projection pursuit learning (PPL). Unlike the totally parametric BPL method, the PPL non-parametrically estimates unknown nonlinear functions sequen(cid:173) tially (neuron-by-neuron and layer-by-Iayer) at each iteration while jointly estimating the interconnection weights. In terms of learning efficiency, both methods have comparable training speed when based on a Gauss(cid:173) Newton optimization algorithm while the PPL is more parsimonious. In terms of learning robustness toward noise outliers, the BPL is more sensi(cid:173) tive to the outliers.
Cite
Text
Huang et al. "A Comparison of Projection Pursuit and Neural Network Regression Modeling." Neural Information Processing Systems, 1991.Markdown
[Huang et al. "A Comparison of Projection Pursuit and Neural Network Regression Modeling." Neural Information Processing Systems, 1991.](https://mlanthology.org/neurips/1991/huang1991neurips-comparison/)BibTeX
@inproceedings{huang1991neurips-comparison,
title = {{A Comparison of Projection Pursuit and Neural Network Regression Modeling}},
author = {Huang, Jenq-Neng and Li, Hang and Maechler, Martin and Martin, R. Douglas and Schimert, Jim},
booktitle = {Neural Information Processing Systems},
year = {1991},
pages = {1159-1166},
url = {https://mlanthology.org/neurips/1991/huang1991neurips-comparison/}
}