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Kernel-Partial Least Squares regression coupled to pseudo-sample trajectories for the analysis of mixture designs of experiments
| Content Provider | Semantic Scholar |
|---|---|
| Author | Vitale, Raffaele Palací-López, Daniel G. Kerkenaar, Harmen H. M. Postma, Geert J. Buydens, Lutgarde M. C. Ferrer, Alberto |
| Copyright Year | 2018 |
| Abstract | Abstract This article explores the potential of Kernel-Partial Least Squares (K-PLS) regression for the analysis of data proceeding from mixture designs of experiments. Gower's idea of pseudo-sample trajectories is exploited for interpretation purposes. The results show that, when the datasets under study are affected by severe non-linearities and comprise few observations, the proposed approach can represent a feasible alternative to classical methodologies (i.e. Scheffe polynomial fitting by means of Ordinary Least Squares - OLS - and Cox polynomial fitting by means of Partial Least Squares - PLS). Furthermore, a way of recovering the parameters of a Scheffe model (provided that it holds and has the same complexity as the K-PLS one) from the trend of the aforementioned pseudo-sample trajectories is illustrated via a simulated case-study. |
| Starting Page | 37 |
| Ending Page | 46 |
| Page Count | 10 |
| File Format | PDF HTM / HTML |
| DOI | 10.1016/j.chemolab.2018.02.002 |
| Volume Number | 175 |
| Alternate Webpage(s) | https://repository.ubn.ru.nl/bitstream/handle/2066/190841/190841%20suppl.pdf?sequence=1 |
| Alternate Webpage(s) | https://repository.ubn.ru.nl/bitstream/handle/2066/190841/190841_suppl.pdf?sequence=1 |
| Alternate Webpage(s) | https://doi.org/10.1016/j.chemolab.2018.02.002 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
