L1-norm Based Two-dimensional Linear Discriminant Analysis

Chen Si-bao; Chen Dao-ran; Luo Bin

doi:10.11999/JEIT141093

Volume 37 Issue 6

Jun. 2015

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Chen Si-bao, Chen Dao-ran, Luo Bin. L1-norm Based Two-dimensional Linear Discriminant Analysis[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1372-1377. doi: 10.11999/JEIT141093

Citation:

Chen Si-bao, Chen Dao-ran, Luo Bin. L1-norm Based Two-dimensional Linear Discriminant Analysis[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1372-1377. doi: 10.11999/JEIT141093

Chen Si-bao, Chen Dao-ran, Luo Bin. L1-norm Based Two-dimensional Linear Discriminant Analysis[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1372-1377. doi: 10.11999/JEIT141093

Citation:

Chen Si-bao, Chen Dao-ran, Luo Bin. L1-norm Based Two-dimensional Linear Discriminant Analysis[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1372-1377. doi: 10.11999/JEIT141093

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L1-norm Based Two-dimensional Linear Discriminant Analysis

doi: 10.11999/JEIT141093

Received Date: 2014-08-18
Rev Recd Date: 2015-02-04
Publish Date: 2015-06-19

Abstract

Abstract

To overcome the curse of dimensionality caused by vectorization of image matrices, and to increase robustness to outliers, L1-norm based Two-Dimensional Linear Discriminant Analysis (2DLDA-L1) is proposed for dimensionality reduction. It makes full use of strong robustness of L1-norm to outliers and noises. Furthermore, it performs dimensionality reduction directly on image matrices. A rapid iterative optimization algorithm, with its proof of monotonic convergence to local optimum, is given. Experiments on several public image databases verify the robustness and the effectiveness of the proposed method.
- Image processing,
- L1-norm,
- Two-Dimensional Linear Discriminant Analysis (2DLDA),
- Linear projection,
- Dimensionality reduction

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References

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