A kernel method for canonical correlation analysis
| dc.creator | Akaho, Shotaro | |
| dc.date | 2006-09-13 | |
| dc.date | 2007-02-14 | |
| dc.date.accessioned | 2026-07-07T07:46:34Z | |
| dc.date.available | 2026-07-07T07:46:34Z | |
| dc.description | Canonical correlation analysis is a technique to extract common features from a pair of multivariate data. In complex situations, however, it does not extract useful features because of its linearity. On the other hand, kernel method used in support vector machine is an efficient approach to improve such a linear method. In this paper, we investigate the effectiveness of applying kernel method to canonical correlation analysis. | |
| dc.description | Full version of paper presented in IMPS2001 (International Meeting of Psychometric Society) 2007-Feb-14: typos in equations (23) and (24) in page 3 of the first version have been corrected | |
| dc.identifier | https://arxiv.org/abs/cs/0609071 | |
| dc.identifier | http://arxiv.org/abs/cs/0609071 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/123870 | |
| dc.subject | Machine Learning | |
| dc.subject | Computer Vision and Pattern Recognition | |
| dc.title | A kernel method for canonical correlation analysis | |
| dc.type | text |