A neural network based on canonical correlation for multicollinearity diagnosis
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 multicollincarity.We develop a second model which not only performs as well on multicollinenr 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 s 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.
Canonical correlation analysis Roughness penalty Multicollinearity Partial least squares regression
Jifu Nong
College of Science Guangxi University for Nationalities Nanning, China
国际会议
太原
英文
855-858
2012-12-08（万方平台首次上网日期，不代表论文的发表时间）