Name
Abstract | ||
|---|---|---|
This paper summarizes a multivariate statistical method called kernel PLS in its use of prediction. PLS has been widely used in the multivariate statistical process monitoring but is only effective in linear condition. This leads to further study and construction of a variant of PLS. Kernel PLS algorithm has been proposed to solve this problem. KPLS establishes relationship between input and output variables in a high-dimensional space. Input data set can be considered as linear in this space. This paper discusses the prediction model construction process and shows nonlinear examples to demonstrate the effectiveness of KPLS. This prediction method has proven to be powerful in many areas, such as chemometrics, bioinformatics and neuroscience. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.neucom.2015.03.028 | Neurocomputing |
Keywords | Field | DocType |
Kernel PLS,Regression,Multivariate statistical process monitoring | Kernel (linear algebra),Nonlinear system,Regression,Multivariate statistics,Input/output,Artificial intelligence,Chemometrics,Statistical process monitoring,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
165 | C | 0925-2312 |
Citations | PageRank | References |
3 | 0.38 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingyu Wang | 1 | 135 | 24.90 |
| Guoyang Yan | 2 | 3 | 0.38 |
| Zhongyang Fei | 3 | 320 | 26.05 |