Fast principal component analysis using fixed-point algorithm
There are no files associated with this record.
| Title | Fast principal component analysis using fixed-point algorithm |
|---|---|
| Author | Sharma, Alokanand; Paliwal, Kuldip Kumar |
| Journal Name | Pattern Recognition Letters |
| Year Published | 2007 |
| Place of publication | Netherlands |
| Publisher | Elsevier BV |
| Abstract | In this paper we present an efficient way of computing principal component analysis (PCA). The algorithm finds the desired number of leading eigenvectors with a computational cost that is much less than that from the eigenvalue decomposition (EVD) based PCA method. The mean squared error generated by the proposed method is very similar to the EVD based PCA method. |
| Peer Reviewed | Yes |
| Published | Yes |
| Publisher URI | http://www.elsevier.com/wps/find/journaldescription.cws_home/505619/description#description |
| Alternative URI | http://dx.doi.org/10.1016/j.patrec.2007.01.012 |
| Volume | 28 |
| Page from | 1151 |
| Page to | 1155 |
| ISSN | 0167-8655 |
| Date Accessioned | 2008-01-24 |
| Date Available | 2009-09-21T05:51:11Z |
| Language | en_AU |
| Research Centre | Institute for Integrated and Intelligent Systems |
| Faculty | Faculty of Science, Environment, Engineering and Technology |
| Subject | PRE2009-Pattern Recognition |
| URI | http://hdl.handle.net/10072/18520 |
| Publication Type | Journal Articles (Refereed Article) |
| Publication Type Code | c1 |
Please use this identifier to cite this record: http://hdl.handle.net/10072/18520
Griffith University copyright notice
Copyright in individual works within the repository belongs to their authors or publishers. You may make a print or digital copy of a work for your personal non-commercial use. All other rights are reserved, except for fair dealings or other user rights granted by the copyright laws of your country.
Footer information
Back to top