期刊论文

You, Xinge

英文

Chemometrics and Intelligent Laboratory Systems

0169-7439

2016

152

42-53

10.1016/j.chemolab.2016.01.004

The main drawbacks of existing methods are as follows: (1) since the sparse constraint is added during each deflation process, the selected variables are different for each time. (2) The selected variables are not highly related with response matrix thus the accuracy is low. (3) The result is not interpretable with all sparse vectors. As shown in Fig. 3, three sparse loading vectors obtained by sparse and MNPLS are plotted in Fig. 3 (a) and (b) respectively. According to Fig. 3 (a), the weighted

本校为其他机构

The partial least squares (PLS) method is designed for prediction problems when the number of predictors is larger than the number of training samples. PLS is based on latent components which are linear combinations of the original predictors, it automatically employs all predictors regardless of their relevance. This strategy will potentially degrade its performance, and make the obtained coefficients lack interpretability. Then, several sparse PLS (SPLS) methods are proposed to simultaneously conduct prediction and variable selection via sparsely combining original predictors. However, if in...