基于免疫克隆的投影寻踪聚类算法及其应用
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摘要
随着Internet技术的出现和通信技术的迅猛发展,人类积累的数据无论从规模上还是维数上都大大增加了,使得一些传统经典的聚类算法的效果急剧下降。为此,本文研究了投影寻踪线性数据降维方法,将免疫克隆算法用于投影寻踪聚类,对纹理图像、SAR图像进行了分割,取得的主要成果如下:
(1)提出了基于免疫克隆的投影寻踪聚类算法,利用免疫克隆算法优化投影寻踪的投影指标,得到最优投影方向以及最优子空间,在最优子空间上获得了较好的聚类结果;
(2)提出基于LDA投影指标的免疫克隆投影寻踪聚类算法,在投影寻踪聚类模型中,使用线性判别分析中的类间散度与类内散度的关系作为投影指标,并利用免疫克隆算法对这些指标进行优化,得到最优子空间。同时,对基于LDA投影指标投影寻踪聚类模型与K-means聚类算法进行自适应迭代优化结果,获得了较优的聚类结果;
(3)根据拉普拉斯图建立有标签数据和无标签数据的关系,提出了基于半监督的投影寻踪聚类算法,并对纹理图像、SAR图像进行分割,实验结果表明,图像的分割精度有所提高;此外,将迁移学习引入基于LDA投影指标的免疫克隆投影寻踪聚类算法,对高光谱遥感图像波段迁移后进行图像分割。结果表明,图像分割精度有所提高。
With the drastic development of Internet and communication, the accumulation of data is increased both in scale and dimension, which lead to the efficiency of some traditional clustering algorithms reduced. In this thesis, the dimensional reduction method of projection pursuit is researched, which is combined with immune clonal method to construct some new algorithms. Then these algorithms are applied in texture image and SAR image segmentation. The main achievements are as follows:
(1)The algorithm of projection pursuit clustering based on immune clonal selection algorithm is proposed. Immune clonal selection algorithm is utilized to optimize the projection index, and then the optimum direction and subspace are obtained. Finally, the better clustering results are acquired in this subspace;
(2) The algorithm of immune clonal selection projection pursuit clustering based on LDA index is proposed. In projection pursuit clustering algorithm model, we adopt Immune clonal selection algorithm to optimize the projection index which is the relationship between with-in scatters and between-in scatters, then the optimum direction and subspace are obtained. Meanwhile, the projection pursuit method and K-means clustering are iterated adaptively, and then the better results are obtained;
(3) Based on the relationship of labeled and unlabeled data which is constructed by graph Laplacian, the algorithm of semi-supervised projection pursuit clustering is proposed. And the algorithm is applied in texture and SAR image segmentation. Seen from the results, the precision are improved. Moreover, transfer learning is introduced to the algorithm of immune clonal selection projection pursuit clustering based on LDA index. Firstly, transferring the band of the hyperspectral remote images, then do image segmentation. The result of segmentation shows higher precision.
(1)提出了基于免疫克隆的投影寻踪聚类算法,利用免疫克隆算法优化投影寻踪的投影指标,得到最优投影方向以及最优子空间,在最优子空间上获得了较好的聚类结果;
(2)提出基于LDA投影指标的免疫克隆投影寻踪聚类算法,在投影寻踪聚类模型中,使用线性判别分析中的类间散度与类内散度的关系作为投影指标,并利用免疫克隆算法对这些指标进行优化,得到最优子空间。同时,对基于LDA投影指标投影寻踪聚类模型与K-means聚类算法进行自适应迭代优化结果,获得了较优的聚类结果;
(3)根据拉普拉斯图建立有标签数据和无标签数据的关系,提出了基于半监督的投影寻踪聚类算法,并对纹理图像、SAR图像进行分割,实验结果表明,图像的分割精度有所提高;此外,将迁移学习引入基于LDA投影指标的免疫克隆投影寻踪聚类算法,对高光谱遥感图像波段迁移后进行图像分割。结果表明,图像分割精度有所提高。
With the drastic development of Internet and communication, the accumulation of data is increased both in scale and dimension, which lead to the efficiency of some traditional clustering algorithms reduced. In this thesis, the dimensional reduction method of projection pursuit is researched, which is combined with immune clonal method to construct some new algorithms. Then these algorithms are applied in texture image and SAR image segmentation. The main achievements are as follows:
(1)The algorithm of projection pursuit clustering based on immune clonal selection algorithm is proposed. Immune clonal selection algorithm is utilized to optimize the projection index, and then the optimum direction and subspace are obtained. Finally, the better clustering results are acquired in this subspace;
(2) The algorithm of immune clonal selection projection pursuit clustering based on LDA index is proposed. In projection pursuit clustering algorithm model, we adopt Immune clonal selection algorithm to optimize the projection index which is the relationship between with-in scatters and between-in scatters, then the optimum direction and subspace are obtained. Meanwhile, the projection pursuit method and K-means clustering are iterated adaptively, and then the better results are obtained;
(3) Based on the relationship of labeled and unlabeled data which is constructed by graph Laplacian, the algorithm of semi-supervised projection pursuit clustering is proposed. And the algorithm is applied in texture and SAR image segmentation. Seen from the results, the precision are improved. Moreover, transfer learning is introduced to the algorithm of immune clonal selection projection pursuit clustering based on LDA index. Firstly, transferring the band of the hyperspectral remote images, then do image segmentation. The result of segmentation shows higher precision.
引文
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[2] J. Han, M. Kamb.数据挖掘:概念与技术(第2版) [M].机械工业出版社.2007.
[3] R. Groth.数据挖掘一构筑企业竞争优势[M].西南交通大学出版社.2001.
[4] D. Hand, H. Mannila, P. Smyth.数据挖掘原理[M].机械工业出版社.2003.
[5] P. Berkhin. A Survey of clustering data mining techniques. Grouping Multi- dimensional Data In Grouping Multidimensional Data , 2006:25-71.
[6] W. Qian, A. Zhou. Analyzing Popular Clustering Algorithrns from Different View -points [J]. Joumal of Sortware, 2002.13 (8):1382-1394.
[7] A. K. Jain, M. N. Murty, P. J. Flynn. Data Clustering: A Review [C]. ACM Comp- uting Surveys, 1999.31 (3):264-323.
[8]孙吉贵,刘杰,赵连宇.聚类算法研究[J].软件学报, 2008. 19(1):48-61.
[9] D. L. Donoho. High-Dimensional Data Analysis: The Curses and Blessings of Di- mensionality [C]. Aide-Memoire of a Lecture at AMS Conference on Math Challenges of the 21st Century, 2000:1-23.
[10] M. Verleysen. Learning High-dimensional Data [Z]. Limitations and Future Trends in Neural Computation, 2003:141-162.
[11] K. Beyer, J. Goldstein, R. Ramakrishnan. When is nearest meaningful[C]. Proceeding of the ICDT, 1999:217-235.
[12] A. Hinneburg, C. C. Aggarwal, D. A. Keim. What is the nearest neighbor in high dimensional spaces[C]. Proceeding of the VLDB, 2000:506-515.
[13] R.Harpaz.A short introduction to subspace clustering [Z].2004.
[14] L. Jing, M. K. Ng, J. Z. Xu. On the Performance of Feature Weighting K-Means for Text Subspace Clustering[C]. Proceeding of the WAIM, 2005:502-512.
[15] L. Parsons, E. Haque, H. Liu. Subspace Clustering for High Dimensional Data:A Review[J]. ACM SIGKDD Explorations Newsletter, 2004, 6(1): 90-105.
[16] R.O. Duda, P. Hart .Use of the Hough transformation to detect lines and curves in pictures[C]. Communication on ACM, 1972, 15(1):11-15.
[17] A. Martelli. Edge detection using heuristic search methods [J].Computer vision, graphic and image processing, 1972, 1(2):169-182.
[18] A. Martelli. An application of heuristic search methods to edge and contourdetection[C]. Communication on ACM, 1976, 19(2):73-83.
[19] R. C. Gonzalez, R. E. Woods, S. L. Eddins. Digital Image Processing Using MATLAB [M].电子工业出版社.
[20] N. R. Pal, S. K. Pal. A review on image segmentation techniques [J]. Pattern Recognition, 1993, 26(9):1277-1294.
[21]章毓晋.图像分割[M].科学出版社,2001.
[22] R. Xu, D. Wunsch II. Survey of Clustering Algorithms [J]. IEEE Transaction on Neural Networks, 2005, 16(3):645-678.
[23] J. H. Friedman, J. W. Tukey.A projection pursuit algorithm for exploratory data analysis [J]. IEEE Trans. of computers, 1974, 23(9): 881-890.
[24] J. H. Friedman.Exploratory projection pursuit [J].ofthe Aniencan Statistical Association, 1987, 82(392): 249-266.
[25] M. C. Jones, R. Sibson.What is projection pursuit? [J]. Journal of the Royal Statistical Society, Se, A, 1987, 150:1-36.
[26]付强,赵小勇著.投影寻踪模型原理及其运用[M].科学出版社.
[27] J. B. Kruskal.Linear transformations of multivariate data to reveal clustering [J]. Multidimensional Scaling: Theory and Application in Behavioral Science, 1972, l(1): 179-191.
[28] P. J. Huber. Projection Pursuit. The Annals of Statistics, 1985, 13(2), 435-525.
[29] S. S. Chiang, C. I. Chang. Unsupervised target detection in hyperspectral image using projection pursuit [J]. IEEE Transactions on geosciences and remote sensing, 2001, 9(7): 1380-1391.
[30] S. S. Chiang, C. I. Chang. Subpixel detection for hyperspectral images using Projection Pursuit[C]. Part of the EUROPTO Conference on Image and Signal Processing for Remote Sensing V. Florence,Italy Sep. 1999.SPIE 3871:107-115.
[31] A. Iafrraguerri, C. I. Chang. Projection pursuit analysis of hyperspectral senses[C]. Part of the SPIE Conference on Algorithms for Multispectral and Hyperspectral Images VI. Orlando. Florida. April 1998.SPIE 3372:51-59.
[32] L. O. Jimenez, D. A. Landgrebe. Hyperspectral data analysis and supervised feature reduction via Projection Pursuit [J]. IEEE Transactions on Geosciences and Remote Sensing, 1999, 37(6): 2653-2667.
[33] L. O. Jimenez, A. Morales-Morel, A. Creus. Classifieation of hyperdimensional data based on feature and decision fusion approaches using projection pursuit [J]. IEEE transactions on geoscience and remote sensing, 1999, 37(3): 1360-1366.
[34] R. A. Fisher. The Statistical Utilization of Multiple Measurements. Annals ofEugenics, 1938, 8, 376-386.
[35] Switzer P. Numerical Classification [A]. Geostatistics, New York: Plenum Press, 1970.
[36] P. Hall. On Polynomial-Based Projection Indices for Exploratory Projection Pursuit. TheAnnals of Statistics, 1989, 17(2), 589-605.
[37] D. Cook, A. Buja, J.Cabrera. Projection Pursuit Indexes Based on Orthogonal Function Expansions [J]. Journal of Computational and Graphical Statistics, 1993, 2(3): 225-250.
[38] G. P. Nason. Three-dimensional projection pursuit [J]. Applied Statistics, 1995, 44 (4):411-430.
[39] J. L. Tu, T. Huang, R.Beveridge, etc. Orthogonal projection pursuit using genetic optimization[C]. Statistical Signal Processing. IEEE, 2003: 266-269.
[40] S. J. Wang, X. L. Zhang, J. Ding. Projection Pursuit Cluster Model and its Application quality assessment [J]. Journal of Yangtze River Scientific Research Institute, 2002, 19(6): 53-55.
[41] J. A .Hartigan, M.A.Wong. A K-means Clustering Algorithm [J]. Applied Statistics, 1979, 28 (1):100-108.
[42] G. Z. Chen, J. Q. Wang, C .J. Li. Solving the Optimization of Projection Pursuit Model Using Improved Ant Colony Algorithm[C].Fourth International Conference on Natural Computation, 2008:521-525.
[43] A. Ifarraguerri, C.I .Chang. Unsupervised Hyperspectral Image Analysis with Projection Pursuit [J]. IEEE Trans. Geosci and Remote Sensing. 2000, 38 (6):2529-2538.
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