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.
翻译:本文中提出了一种主要模型分析方法,在拟议的PMA算法中,将CPA和PLS结合起来;在方法中,对多个PLS模型进行了次级培训,这些培训是根据随机抽样和替代方法的原始培训组合得出的;所有子PLS模型的回归系数都结合在一个联合回归系数矩阵中;然后,通过在联合回归系数矩阵中执行CPA来估计最后预测方向;拟议的PMA方法与其他传统的减少维度方法进行比较,如PLS、Blagg PLS、线性磁分析(LDA)和PLS-LDA。 6个公共数据集的实验结果显示,我们拟议的方法能够取得更好的分类性能,通常比较稳定。