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Margin Based Dimensionality Reduction and Generalization
| Content Provider | Semantic Scholar |
|---|---|
| Author | Peng, Jing Robila, Stefan A. Seetharaman, Guna |
| Copyright Year | 2010 |
| Abstract | Linear discriminant analysis (LDA) for dimension reduction has been applied to a wide variety of problems such as face recognition. However, it has a major computational difficulty when the number of dimensions is greater than the sample size. In this paper, we propose a margin based criterion for linear dimension reduction that addresses the above problem associated with LDA. We establish an error bound for our proposed technique by showing its relation to least squares regression. In addition, there are well established numerical procedures such as semi-definite programming for optimizing the proposed criterion. We demonstrate the efficacy of our proposal and compare it against other competing techniques using a number of examples. |
| Starting Page | 55 |
| Ending Page | 64 |
| Page Count | 10 |
| File Format | PDF HTM / HTML |
| DOI | 10.2174/1874061801004010055 |
| Alternate Webpage(s) | https://benthamopen.com/contents/pdf/TOAIJ/TOAIJ-4-55.pdf |
| Alternate Webpage(s) | http://www.dtic.mil/dtic/tr/fulltext/u2/a530724.pdf |
| Alternate Webpage(s) | http://benthamopen.com/contents/pdf/TOAIJ/TOAIJ-4-55.pdf |
| Alternate Webpage(s) | https://doi.org/10.2174/1874061801004010055 |
| Volume Number | 4 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |