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基于PSO的Fisher准则下小样本最佳鉴别变换*

, PP. 288-292

Keywords: 模式识别,Fisher准则,最佳鉴别变换,粒子群优化(PSO)

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Abstract:

小样本条件下,Fisher准则中类内散布矩阵一般是奇异的,无法直接求解.本文提出利用粒子群优化理论,在无需求类内散布矩阵逆的情况下求解Fisher准则下小样本最佳鉴别变换的方法.讨论了通过粒子群优化算法的位置——速度搜索模型获取最佳鉴别投影向量的方法和步骤.实验对比类内散布矩阵非奇异时,采用计算特征向量方法和本文方法的差异.分析验证小样本条件下类内散布矩阵奇异时,通过本文方法进行最佳鉴别变换的分类效果.实验证实本文算法的有效性.

References

[1]  Tian Q, Fainman Y, Gu Z H, et al. Comparison of Statistical Pattern Recognition Algorithms for Hybrid Processing, Part II: Eigenvector-Based Algorithms. Journal of the Optical Society of America, 1988, 5(10): 1670-1672
[2]  Hong Ziquan,Yang Jingyu. Optimal Discriminant Plane for a Small Number of Samples and Design Method of Classifier on the Plane. Pattern Recognition, 1991, 24(4): 314-324
[3]  Cheng Yongqing, Zhuang Yongming, Yang Jingyu. Optimal Fisher Discriminant Analysis Using the Rank Decomposition. Pattern Recognition, 1992, 25(1): 101-111
[4]  Kennedy J, Eberhart R C. Particle Swarm Optimization // Proc of the IEEE International Conference on Neural Networks. Perth Western, Australia, 1995, Ⅳ: 1942-1946
[5]  Zitzler E, Deb K, Thiele L. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 2000, 8(2): 173-195
[6]  Ciuprina G, Laon D, Munteanu I. Use of Intelligent-Particle Swarm Optimization in Electromagnetics. IEEE Trans on Magnetics, 2002, 38(2): 1037-1040
[7]  Mendes R, Kennedy J, Neves J. The Fully Informed Particle Swarm: Simpler, Maybe Better. IEEE Trans on Evolutionary Computation, 2004, 8(3): 204-210
[8]  Liang J J, Qin A K. Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multi-Modal Functions. IEEE Trans on Evolutionary Computation, 2006, 10(3): 281-295
[9]  Lü Yanping, Li Shaozi, Chen Shuili, et al. Particle Swarm Optimization Based on Adaptive Diffusion and Hybrid Mutation. Journal of Software, 2007, 18(11): 2740-2751 (in Chinese) (吕艳萍,李绍滋,陈水利,等.自适应扩散混合变异机制微粒群算法.软件学报, 2007, 18(11): 2740-2751)
[10]  Rui Ting, Shen Chunlin, Qi Tian, et al. Comparison and Analysis on ICA & PCA's Ability in Feature Extraction. Pattern Recognition and Artificial Intelligence, 2005, 18(1): 124-128 (in Chinese) (芮 挺,沈春林,Qi Tian,等.ICA与PCA特征抽取能力的比较分析.模式识别与人工智能, 2005, 18(1): 124-128)
[11]  Cortes C, Vapnik V. Support Vector Networks. Machine Learning, 1995, 20(3): 273-295

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