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| L1-norm Based Two-dimensional Linear Discriminant Analysis |
| Chen Si-bao Chen Dao-ran Luo Bin |
(School of Computer Science and Technology, Anhui University, Hefei 230601, China)
(Key Laboratory for Industrial Image Processing and Analysis of Anhui Province, Hefei 230039, China) |
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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.
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Received: 18 August 2014
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Corresponding Authors:
Chen Si-bao
E-mail: sbchen@ahu.edu.cn
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