Partial Least Squares Method

Before performing linear regression analysis based on least squares, we can use the Partial Least Squares method to conduct principal component analysis for dimensionality reduction on datasets. The PLS method essentially projects the original dataset into a new coordinate system, where the selection of this new coordinate system is achieved by maximizing the covariance between two datasets. This common data preprocessing technique can improve model accuracy and reduce the risk of overfitting. The implementation typically involves calculating covariance matrices, performing eigenvalue decomposition, and selecting principal components based on variance thresholds. The projection transformation is achieved through linear combinations of original variables that maximize the explained variance. The following source code is provided in its complete, unedited form by the GreenSim team for free use. If you wish to redistribute this code, please provide proper attribution to the GreenSim team (http://blog.sina.com.cn/greensim). We hope this code will be helpful in your work, and we welcome any valuable suggestions and feedback you may have regarding the implementation.