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基于L1-范数的二维线性判别分析

陈思宝 陈道然 罗斌

引用本文: 陈思宝, 陈道然, 罗斌. 基于L1-范数的二维线性判别分析[J]. 电子与信息学报, 2015, 37(6): 1372-1377. doi: 10.11999/JEIT141093 shu
Citation:  Chen Si-bao, Chen Dao-ran, Luo Bin. L1-norm Based Two-dimensional Linear Discriminant Analysis[J]. Journal of Electronics and Information Technology, 2015, 37(6): 1372-1377. doi: 10.11999/JEIT141093 shu

基于L1-范数的二维线性判别分析

摘要: 为了避免图像数据向量化后的维数灾难问题,以及增强对野值(outliers)及噪声的鲁棒性,该文提出一种基于L1-范数的2维线性判别分析(L1-norm-based Two-Dimensional Linear Discriminant Analysis, 2DLDA-L1)降维方法。它充分利用L1-范数对野值及噪声的强鲁棒性,并且直接在图像矩阵上进行投影降维。该文还提出一种快速迭代优化算法,并给出了其单调收敛到局部最优的证明。在多个图像数据库上的实验验证了该方法的鲁棒性与高效性。

English

    1. [1]

      Duda R, Hart P, and Stork D. Pattern Classification[M]. Second edition, New York: John Wiley Sons, 2001: 2-5.

    2. [2]

      Chan L, Salleh S, Ting C, et al.. Face identification and verification using PCA and LDA[C]. International Symposium on Information Technology, Kuala Lumpur, Malaysia, 2008: 1-6.

    3. [3]

      Rujirakul K, So-In C, Arnonkijpanich B, et al.. PFP-PCA: parallel fixed point PCA face recognition[C]. 2013 4th International Conference on Intelligent Systems, Modelling and Simulation, Bangkok, 2013: 29-31.

    4. [4]

      Belhumeur P, Hespanha J, and Kriegman D. Eigenfaces vs. fisherfaces: recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720.

    5. [5]

      Yang Jian, Zhang D, Frangi A, et al.. Two-dimensional PCA: a new approach to appearance-based face representation and recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131-137.

    6. [6]

      Wang Shi-min, Ye Ji-hua, and Ying De-quan. Research of 2DPCA principal component uncertainty in face recognition[C]. 2013 8th International Conference on Computer Science Education, Colombo, Sri Lanka, 2013: 159-162.

    7. [7]

      Li Ming and Yuan Bao-zong. A novel statistical linear discriminant analysis for image matrix: two-dimensional Fisherfaces[J]. Pattern Recognition Letters, 2005, 26(5): 527-532.

    8. [8]

      Mahanta M and Plataniotis K. Ranking 2DLDA features based on fisher discriminance[C]. IEEE International Conference on Acoustic, Speech and Signal Processing, Florence, Italy, 2014: 4-9.

    9. [9]

      Wang Bin-bin, Hao Xin-jie, Chen Li-sheng, et al.. Face recognition based on the feature fusion of 2DLDA and LBP[C]. 2013 Fourth International Conference on Information, Intelligence, Systems and Applications, Piraeus, Greece, 2013: 10-12.

    10. [10]

      Pang Yan-wei, Li Xue-long, and Yuan Yuan. Robust tensor analysis with L1-norm[J]. IEEE Transactions on Circuits Syst ems for Video Techology, 2010, 20(2): 172-178.

    11. [11]

      Zheng Wen-ming, Lin Zhou-chen, and Wang Hai-xian. L1-norm kernel discriminant analysis via Bayes error bound optimization for robust feature extraction[J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(4): 793-805.

    12. [12]

      Kwak N. Principal component analysis based on L1-norm maximization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(9): 1672-1680.

    13. [13]

      Li Xi, Hu Wei-ming, Wang Han-zi, et al.. Linear discriminant analysis using rotational invariant L1 norm[J]. Neurocomputing, 2010, 73(13): 2571-2579.

    14. [14]

      Wang Hai-xian, Lu Xue-song, Hu Zi-lan, et al.. Fisher discriminant analysis with L1-norm[J]. IEEE Transactions on Cybernetics, 2013, 44(6): 828-842.

    15. [15]

      Zhong Fu-jin and Zhang Jia-shu. Linear discriminant analysis based on L1-norm maximization[J]. IEEE Transactions on Image Processing, 2013, 22(8): 3018-3027.

    16. [16]

      Li Xue-long, Pang Yan-wei, and Yuan Yuan. L1-norm-based 2DPCA[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2010, 40(4): 1170-1175.

    17. [17]

      Wang Hai-xian, Tang Qin, and Zheng Wen-ming. L1-norm- based common spatial patterns[J]. IEEE Transactions on Biomedical Engineering, 2012, 59(3): 653-662.

    18. [18]

      Jenatton R, Obozinski G, and Bach F. Structured sparse principal component analysis[C]. International Conference on Artificial Intelligence and Statistics, Paris, France, 2009: 1-8.

    19. [19]

      Gross R, Matthews I, Cohn J, et al.. Multi-PIE[C]. 8th IEEE International Conference on Automatic Face Gesture Recognition, Amsterdam, The Netherland, 2008: 1-8.

    20. [20]

      Martinez A and Benavente R. The AR face database[R]. CVC Technical Report 24, Barcelona, Spain, 1998.

    21. [21]

      Samaria F and Harter A. Parameterisation of a stochastic model for human face identification[C]. Proceedings of the Second IEEE Workshop on Applications of Computer Vision, Sarasota, USA, 1994, 138-142.

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文章相关
  • 收稿日期:  2014-08-18
  • 录用日期:  2015-02-04
  • 刊出日期:  2015-06-19
通讯作者: 陈斌, bchen63@163.com
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