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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Nam Nguyen Wanquan Liu Venkatesh, S. |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Comput., Curtin Univ., Bentley, WA, Australia (Nam Nguyen; Wanquan Liu; Venkatesh, S.) |
| Abstract | Two dimensional linear discriminant analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem. Based on these observations, we propose two novel algorithms called regularized 2DLDA and Ridge Regression for 2DLDA (RR-2DLDA). Regularized 2DLDA is an extension of 2DLDA with the introduction of a regularization parameter to deal with the small sample size problem. RR-2DLDA integrates ridge regression into Regularized 2DLDA to balance the distances among different classes after the transformation. These proposed algorithms overcome the limitations of 2DLDA and boost recognition accuracy. The experimental results on the Yale, PIE and FERET databases showed that RR-2DLDA is superior not only to 2DLDA but also other state-of-the-art algorithms. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 880944 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424421749 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2008.4761898 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-12-08 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Boosting Linear discriminant analysis Covariance matrix Face recognition Computational efficiency Image databases Vectors Principal component analysis Strontium |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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