A neural network based on canonical correlation for multicollinearity diagnosis

DOI

10.2991/iccia.2012.208How to use a DOI?

Keywords

Canonical correlation analysis, Roughness penalty, Multicollinearity, Partial least squares regression

Abstract

We review a recent neural implementation of Canonical Correlation Analysis and show, using ideas suggested by Ridge Regression, how to make the algorithm robust. The network is shown to operate on data sets which exhibit multicollinearity. We develop a second model which not only performs as well on multicollinear data but also on general data sets. This model allows us to vary a single parameter so that the network is capable of performing Partial Least Squares regression to Canonical Correlation Analysis and every intermediate operation between the two. On multicollinear data, the parameter setting is shown to be important but on more general data no particular parameter setting is required. Finally, we develop a second penalty term which acts on such data as a smoother in that the resulting weight vectors are much smoother and more interpretable than the weights without the robustification term. We illustrate our algorithms on both artificial and real data.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jifu Nong
PY  - 2014/05
DA  - 2014/05
TI  - A neural network based on canonical correlation for multicollinearity diagnosis
BT  - Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)
PB  - Atlantis Press
SP  - 855
EP  - 858
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccia.2012.208
DO  - 10.2991/iccia.2012.208
ID  - Nong2014/05
ER  -