A note on kernel principal component regression
Kernel principal component regression (KPCR) was studied by Rosipal et al. [18, 19, 20], Hoegaerts et al. [7], and Jade et al. [8]. However, KPCR still encounters theoretical difficulties in the procedure for constructing KPCR and in the choice rule for the retained number of principal components. I...
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Springer
2012
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| author | Wibowo, Antoni Yamamoto, Yoshitsugu |
| author_facet | Wibowo, Antoni Yamamoto, Yoshitsugu |
| author_sort | Wibowo, Antoni |
| collection | ePrints |
| description | Kernel principal component regression (KPCR) was studied by Rosipal et al. [18, 19, 20], Hoegaerts et al. [7], and Jade et al. [8]. However, KPCR still encounters theoretical difficulties in the procedure for constructing KPCR and in the choice rule for the retained number of principal components. In this paper, we revise the method of KPCR to overcome the difficulties. The performance of the revised method is compared to linear regression, nonlinear regression based on Gompertz function, and nonparametric Nadaraya-Watson regression, and gives better results than those of the three methods. |
| first_indexed | 2024-03-05T18:52:05Z |
| format | Article |
| id | utm.eprints-32762 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:52:05Z |
| publishDate | 2012 |
| publisher | Springer |
| record_format | dspace |
| spelling | utm.eprints-327622018-11-30T06:31:17Z http://eprints.utm.my/32762/ A note on kernel principal component regression Wibowo, Antoni Yamamoto, Yoshitsugu QA75 Electronic computers. Computer science Kernel principal component regression (KPCR) was studied by Rosipal et al. [18, 19, 20], Hoegaerts et al. [7], and Jade et al. [8]. However, KPCR still encounters theoretical difficulties in the procedure for constructing KPCR and in the choice rule for the retained number of principal components. In this paper, we revise the method of KPCR to overcome the difficulties. The performance of the revised method is compared to linear regression, nonlinear regression based on Gompertz function, and nonparametric Nadaraya-Watson regression, and gives better results than those of the three methods. Springer 2012-07 Article PeerReviewed Wibowo, Antoni and Yamamoto, Yoshitsugu (2012) A note on kernel principal component regression. Computational Mathematics and Modeling, 23 (3). pp. 350-367. ISSN 1046-283X http://dx.doi.org/ 10.1007/s10598-012-9143-0 DOI:10.1007/s10598-012-9143-0 |
| spellingShingle | QA75 Electronic computers. Computer science Wibowo, Antoni Yamamoto, Yoshitsugu A note on kernel principal component regression |
| title | A note on kernel principal component regression |
| title_full | A note on kernel principal component regression |
| title_fullStr | A note on kernel principal component regression |
| title_full_unstemmed | A note on kernel principal component regression |
| title_short | A note on kernel principal component regression |
| title_sort | note on kernel principal component regression |
| topic | QA75 Electronic computers. Computer science |
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