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基于局部密度估计和近邻关系传播的谱聚类*

, PP. 856-864

Keywords: 谱聚类,密度估计,近邻关系传播,相似度矩阵

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Abstract:

对密度分布不均匀的数据采用近邻传播的谱聚类,存在误将不同类的样本传入同一高相似度的子集中的情况,因而得不到真实的相似度矩阵和准确的聚类结果.针对这一问题,提出一种基于局部密度估计和近邻关系传播的谱聚类(LDENP-SC)算法.该算法首先对样本进行密度估计并升维,然后对新数据采用传播算法更新相似度矩阵并谱聚类.在计算密度时提出一种简易的局部密度计算方法,该方法既能反应样本的密度又能减少运算时间;在更新相似度矩阵时基于传播算法提出一种更新子集间样本相似性的方法,使更新后样本的相似度更接近实际.实验结果表明,LDENP-SC算法能够得出取得理想的相似度矩阵和准确的聚类结果,具有较好的泛化能力,且对一定范围内的参数σ表现出鲁棒性.

References

[1]  Luxburg U V. A Tutorial on Spectral Clustering. Technical Report, No.TR-149. Tübingen, Germany: Max Planck Institute for Biological Cybernetics, 2006
[2]  Shi J B, Malik J. Normalized Cuts and Image Segmentation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905
[3]  Ng A Y, Jordan M I, Weiss Y. On Spectral Clustering: Analysis and an Algorithm // Dietterich T G, Becker S, Ghahramani Z, eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2001: 849-856
[4]  Fowlkes C, Belongie S, Chung F, et al. Spectral Grouping Using the Nystrm Method. IEEE Trans on Pattern Analysis and Machine Intelligence, 2004, 26(2): 214-225
[5]  Sharon E, Galun M, Sharon D, et al. Hierarchy and Adaptivity in Segmenting Visual Scenes. Nature, 2006, 442(7104): 810-813
[6]  Xia T, Cao J, Zhang Y D, et al. On Defining Affinity Graph for Spectral Clustering through Ranking on Manifolds. Neurocomputing, 2009, 72(13/14/15): 3203-3211
[7]  Zhang X C, Li J W, Yu H. Local Density Adaptive Similarity Measurement for Spectral Clustering. Pattern Recognition Letters, 2011, 32(2): 352-358
[8]  Li X Y, Guo L J. Constructing Affinity Matrix in Spectral Clustering Based on Neighbor Propagation. Neurocomputing, 2012, 97(15): 125-130
[9]  Ozertem U, Erdogmus D, Jenssen R. Mean Shift Spectral Clustering. Pattern Recognition, 2008, 41(6): 1924-1938
[10]  Song Y Q, Xie C H, Zhu Y Q, et al. Research on Medical Image Clustering Based on Approximate Density Function. Journal of Computer Research and Development, 2006, 43(11): 1947-1952 (in Chinese)(宋余庆,谢从华,朱玉全,等.基于近似密度函数的医学图像聚类分析研究.计算机研究与发展, 2006, 43(11): 1947-1952)
[11]  Meila M, Shi J B. A Random Walks View of Spectral Segmentation // Proc of the 8th International Workshop on Artificial Intelligence and Statistics. Florida, USA, 2001
[12]  Meila M, Xu L. Multiway Cuts and Spectral Clustering. Technical Report, No.TR-217. Washington, USA: University of Washington, 2003
[13]  Wang L, Bo L F, Jiao L C. Density-Sensitive Spectral Clustering. Acta Electronica Sinica, 2007, 35(8): 1577-1581 (in Chinese)(王 玲,薄列峰,焦李成.密度敏感的谱聚类.电子学报, 2007, 35(8): 1577-1581)
[14]  Meila M. Comparing Clusterings by the Variation of Information // Proc of the 16th Annual Conference on Learning Theory and 7th Kernel Workshop. Washington, USA, 2003: 173-187
[15]  Kong W Z, Sun Z H, Yang C, et al. Automatic Spectral Clustering Based on Eigengap and Orthogonal Eigenvector. Acta Electronica Sinica, 2010, 38(8): 1880-1885,1891 (in Chinese)(孔万增,孙志海,杨 灿,等.基于本征间隙与正交特征向量的自动谱聚类.电子学报, 2010, 38(8): 1880-1885,1891)
[16]  Tian Z, Li X B, Ju Y W. Spectral Matrix Perturbation Analysis. SCIENTIA SINICA Information, 2007, 37(4): 527- 543 (in Chinese)(田 铮,李小斌,句彦伟.谱聚阵的扰动分析.中国科学E辑:信息科学, 2007, 37(4): 527- 543)
[17]  Sun J G. Matrix Perturbation Analysis. Beijing, China: Science Press, 2001: 146-160 (in Chinese)(孙继广.矩阵扰动分析.北京:科学出版社, 2001: 146-160)

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