M. Forina et al., Iterative predictor weighting (IPW) PLS: A technique for the elimination of useless predictors in regression problems, J CHEMOMETR, 13(2), 1999, pp. 165-184
A new method for the elimination of useless predictors in multivariate regr
ession problems is proposed. The method is based on the cyclic repetition o
f PLS regression. In each cycle the predictor importance (product of the ab
solute value of the regression coefficient and the standard deviation of th
e predictor) is computed, and in the next cycle the predictors are multipli
ed by their importance. The algorithm converges after 10-20 cycles. A reduc
ed number of relevant predictors is retained in the final model, whose pred
ictive ability is acceptable, frequently better than that of the model buil
t with all the predictors. Results obtained on many real and simulated data
are presented, and compared with those obtained from other techniques. Cop
yright (C) 1999 John Wiley & Sons, Ltd.