Abstract
Recently, Frey & Dueck proposed a novel clustering algorithm named affinity propagation (AP), which has been shown to be powerful as it costs much less time and reaches much lower error. However, its convergence property has not been studied in theory. In this paper, we focus on convergence property of the algorithm. The properties of the decision matrix when the affinity propagation algorithm converges are given, and the criterion that affinity propagation without the damping factor oscillates is obtained. Based on such results, we point out that damping factor might be important to alleviate oscillation of the affinity propagation, but it is not necessary to add a tiny amount of noise to a similarity matrix.
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References
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3), 264–323 (1999)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007); (Supporting material is available on Science Online)
Mezard, M.: Where are the exemplars? Science 315, 949–951 (2007)
Xiao, J.X., Wang, J.D., Tan, P., Quan, L.: Joint affinity propagation for multiple view segmentation. In: Proc. of ICCV, pp. 1–7 (2007)
An, S., Liu, W.Q., Venkatesh, S.: Acquiring critical light points for illumination subspaces of face images by affinity propagation clustering. In: Ip, H.H.-S., Au, O.C., Leung, H., Sun, M.-T., Ma, W.-Y., Hu, S.-M. (eds.) PCM 2007. LNCS, vol. 4810, pp. 647–654. Springer, Heidelberg (2007)
Michael, J.B., Hans, F.K.: Comment on clustering by passing messages between data points. Science 319, 726c (2008)
Frey, B.J., Dueck, D.: Response to comment on clustering by passing messages between data points. Science 319, 726d (2008)
Michele, L.S., Martin, W.: Clustering by soft-constraint affinity propagation: applications to gene-expression data. Bioinformatics 23(20), 2708–2715 (2007)
Lai, D.R., Lu, H.T.: Identification of community structure in complex networks using affinity propagation clustering method. Modern Physics Letters B 22(16), 1547–1566 (2008)
Jia, Y.Q., Wang, G.D., Zhang, C.H., Hua, H.S.: Finding Image Exemplars Using Fast Sparse Affinity Propagation. In: Proc. of ACM Multimedia, pp. 639–642 (2008)
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Yu, J., Jia, C. (2009). Convergence Analysis of Affinity Propagation. In: Karagiannis, D., Jin, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2009. Lecture Notes in Computer Science(), vol 5914. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10488-6_9
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DOI: https://doi.org/10.1007/978-3-642-10488-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10487-9
Online ISBN: 978-3-642-10488-6
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