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Proceedings Paper

Robust partial least-squares regression: a modular neural network approach
Author(s): Thomas M. McDowall; Fredric M. Ham
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Paper Abstract

We have developed a robust Partial Least-Squares Regression (PLSR) neural network approach to statistical calibration model development. Generalized neural network learning rules derived from a weighted statistical representation error criterion that grows less than quadratically are presented. This optimization criterion allows for higher-order statistics associated with the inputs to be taken into account and also serves to robustify the results when the empirical data contains impulsive and colored noise and outliers. The learning rules presented are considered generalized because they can be used to implement several specialized cases including: robust PLSR, linear PLSR, weighted least-squares, and variance scaling. The same learning rules also implement steepest descent or Newton's method. Newton's method can be used to formulate an adaptive learning rate for training the network.

Paper Details

Date Published: 4 April 1997
PDF: 12 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271496
Show Author Affiliations
Thomas M. McDowall, Florida Institute of Technology (United States)
Fredric M. Ham, Florida Institute of Technology (United States)


Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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