  Motivated by the Bagging Partial Least Squares (PLS) and Principal Component
Analysis (PCA) algorithms, we propose a Principal Model Analysis (PMA) method
in this paper. In the proposed PMA algorithm, the PCA and the PLS are combined.
In the method, multiple PLS models are trained on sub-training sets, derived
from the original training set based on the random sampling with replacement
method. The regression coefficients of all the sub-PLS models are fused in a
joint regression coefficient matrix. The final projection direction is then
estimated by performing the PCA on the joint regression coefficient matrix. The
proposed PMA method is compared with other traditional dimension reduction
methods, such as PLS, Bagging PLS, Linear discriminant analysis (LDA) and
PLS-LDA. Experimental results on six public datasets show that our proposed
method can achieve better classification performance and is usually more
stable.
