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自适应正则化核二维判别分析*

, PP. 1089-1097

Keywords: 核函数,判别分析,降维,半监督学习,自适应正则化

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Abstract:

传统的半监督降维技术中,在原特征空间中定义流形正则化项,但其构造无助于接下来的分类任务.针对此问题,文中提出一种自适应正则化核二维判别分析算法.首先每个图像矩阵经奇异值分解为两个正交矩阵与一个对角矩阵的乘积,通过两个核函数将两个正交矩阵列向量从原始非线性空间映射到一个高维特征空间.然后在低维特征空间中定义自适应正则化项,并将其与二维矩阵非线性方法整合于单个目标函数中,通过交替优化技术,在两个核子空间提取判别特征.最后在两个人脸数据集上的实验表明,文中算法在分类精度上获得较大提升.

References

[1]  Turk M, Pentland A. Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 1991, 3(1): 71-86
[2]  Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720
[3]  Ji S W, Ye J P. Generalized Linear Discriminant Analysis: A Unified Framework and Efficient Model Selection. IEEE Trans on Neural Networks, 2008, 19(10): 1768-1782
[4]  Yang J, Zhang D, Frangi A F, et al. Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Re-cognition. IEEE Trans on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131-137
[5]  Yang J, Zhang D, Xu Y, et al. Two-Dimensional Discriminant Transform for Face Recognition. Pattern Recognition, 2005, 38(7): 1125-1129
[6]  Zhu F M, Zhang D Q. Semi-supervised Dimensionality Reduction Algorithm of Tensor Image. Pattern Recognition and Artificial Inte-lligence, 2009, 22(4): 574-580 (in Chinese)(朱凤梅,张道强.张量图像上的半监督降维算法.模式识别与人工智能, 2009, 22(4): 574-580)
[7]  Schlkopf B, Smola A, Müller K R. Kernel Principal Component Analysis // Proc of the 7th International Conference on Artificial Neural Networks. Lausanne, Switzerland, 1997: 583-588
[8]  Xu D, Yan S C. Semi-supervised Bilinear Subspace Learning. IEEE Trans on Image Processing, 2009, 18(7): 1671-1676
[9]  Mika S, Ratsch G, Weston J, et al. Fisher Discriminant Analysis with Kernels // Proc of the IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing. Madison, USA, 1999, Ⅸ: 41-48
[10]  Yan S C, Xu D, Zhang L, et al. Coupled Kernel-Based Subspace Learning // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA, 2005, I: 645-650
[11]  Cai D, He X F, Han J W. Semi-supervised Discriminant Analysis[EB/OL]. [2013-08-30]. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4408856
[12]  Yin X S, Hu E L. Semi-supervised Locality Dimensionality Reduction. Journal of Image and Graphics, 2011, 16(9): 1615-1624 (in Chinese)(尹学松,胡恩良.半监督局部维数约减.中国图象图形学报, 2011, 16(9): 1615-1624)
[13]  Belkin M, Niyogi P, Sindhwani V. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. Journal of Machine Learning Research, 2006, 7(11): 2399-2434
[14]  Zhou D Y, Bousquet O, Lal T N, et al. Learning with Local and Global Consistency[EB/OL]. [2013-08-30]. http://research.microsoft.com/en-us/um/people/denzho/papers/LLGC.pdf
[15]  Zhang L M, Qiao L S, Chen S C. Graph-Optimized Locality Preserving Projections. Pattern Recognition, 2010, 43(6): 1993-2002
[16]  He X F, Niyogi P. Locality Preserving Projections[EB/OL]. [2003-08-30]. http://papers.nips.cc/paper/2359-locality-preserving-projections.pdf

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