Approximate LDA Technique for Dimensionality Reduction in the Small Sample Size Case
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| Title | Approximate LDA Technique for Dimensionality Reduction in the Small Sample Size Case |
|---|---|
| Author | Paliwal, Kuldip Kumar; Sharma, Alok |
| Journal Name | Journal of Pattern Recognition Research |
| Year Published | 2011 |
| Place of publication | United States |
| Publisher | JPRR |
| Abstract | The regularized linear discriminant analysis (LDA) technique overcomes the small sample size (SSS) problem by adding a regularization parameter to the eigenvalues of within-class scatter matrix. However, it has some drawbacks. In this paper we address its drawbacks and propose an improvement. The proposed technique is experimented on several datasets and promising results have been obtained. |
| Peer Reviewed | Yes |
| Published | Yes |
| Publisher URI | http://www.jprr.org/index.php/jprr/article/view/370 |
| Volume | X |
| Page from | 298 |
| Page to | 306 |
| ISSN | 1558-884X |
| Date Accessioned | 2012-05-30; 2012-06-26T00:52:39Z |
| Date Available | 2012-06-26T00:52:39Z |
| Research Centre | Institute for Integrated and Intelligent Systems |
| Faculty | Faculty of Science, Environment, Engineering and Technology |
| Subject | Artificial Intelligence and Image Processing |
| URI | http://hdl.handle.net/10072/45639 |
| Publication Type | Journal Articles (Refereed Article) |
| Publication Type Code | c1 |
Please use this identifier to cite this record: http://hdl.handle.net/10072/45639
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