Abstract
This paper presents a multi-scale, hierarchical framework to extend the scalability of support vector clustering (SVC). Based on the multi-sphere support vector clustering, the clustering algorithm called multi-scale multi-sphere support vector clustering (MMSVC) in this framework works in a coarse-to-fine and top-to-down manner. Given one parent cluster, the next learning scale is generated by a secant-like numerical algorithm. A local quantity called spherical support vector density (sSVD) is proposed as a cluster validity measure which describes the compactness of the cluster. It is used as a terminate term in our framework. When dealing with large-scale dataset, our method benefits from the online learning, easy parameters tuning and the learning efficiency. 1.5 million tiny images were used to evaluate the method. Experimental results demonstrate that the method greatly improves the scalability and learning efficiency of support vector clustering.
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References
Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V., Critianini, N., Shawe-Taylor, J., Williamson, B.: Support Vector Clustering. Journal of Machine Learning Research 2, 125–137 (2002)
Dhillon, I.S., Guan, Y., Kulis, B.: Kernel k-means: spectral clustering and normalized cuts. In: ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 551–556 (2004)
Fischer, B., Roth, V., Buhmann, J.M.: Clustering with the Connectivity Kernel. In: Advances in Neural Information Processing Systems, vol. 16, pp. 1–16 (2004)
Girolami, M.: Mercer kernel-based clustering in feature space. In: Proceedings of 2004 IEEE International Joint Conference on Neural Networks, 2004, vol. 13, pp. 780–784 (2002)
Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.: A support vector clustering method. In: Pattern Recognition, vol. 722, pp. 724–727 (2000)
Tax, D.M.J., Duin, R.P.W.: Support Vector Data Description. Machine Learning 54, 45–66 (2004)
Camastra, F., Verri, A.: A novel kernel method for clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 801–805 (2005)
Defeng, W., Daniel, S.Y., Eric, C.C.T.: Structured One-Class Classification. IEEE Transactions on Systems, Man, and Cybernetics 36, 1283–1295 (2006)
Jung-Hsien, C., Pei-Yi, H.: A new kernel-based fuzzy clustering approach: support vector clustering with cell growing. Fuzzy Systems 11, 518–527 (2003)
Daewon, L., Jaewook, L.: Domain described support vector classifier for multi-classification problems. Pattern Recognition 40, 41–51 (2007)
Chang, L., Deng, X.M., Zheng, S.W., Wang, Y.Q.: Scaling up Kernel Grower Clustering Method for Large Data Sets via Core-sets. Acta Automatica Sinica 34, 376–382 (2008)
Jen-Chieh, C., Jeen-Shing, W.: Support Vector Clustering with a Novel Cluster Validity Method. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2006, vol. 5, pp. 3715–3720 (2006)
Jaewook, L., Daewon, L.: An improved cluster labeling method for support vector clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 461–464 (2005)
Lee, S.H., Daniels, K.M.: Cone Cluster Labeling for Support Vector Clustering. In: Proceedings of the 6th SIAM International Conference on Data Mining (2006)
Lee, S.H., Daniels, K.M.: Gaussian Kernel Width Generator for Support Vector Clustering. In: International Conference on Bioinformatics and its Applications, pp. 151–162 (2004)
Grira, N., Crucianu, M., Boujemaa, N.: Unsupervised and Semi-supervised Clustering: a Brief Survey. A Review of Machine Learning Techniques for Processing Multimedia Contents. Report of the MUSCLE European Network of Excellence (FP6) (2004)
Cao, F., Delon, J., Desolneux, A., Mus, P., Sur, F.: An a contrario approach to hierarchical clustering validity assessment (2004)
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: Cluster validity methods: part I. ACM SIGMOD Record. 31, 40–45 (2002)
Wang, J.-S., Chiang, J.-C.: A cluster validity measure with a hybrid parameter search method for the support vector clustering algorithm. Pattern Recognition 41, 506–520 (2008)
Torralba, A., Fergus, R., Freeman, W.T.: Tiny Images. Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology (2007)
Hansen, M.S., Holm, D.A., Sjöstrand, K., Ley, C.D., Rowland, I.J., Larsen, R.: Multiscale hierarchical support vector clustering. In: Medical Imaging 2008: Image Processing 6914, 69144B, pp. 136–144 (2008)
Sjöstrand, K., Larsen, R.: The Entire Regularization Path for the Support Vector Domain Description. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 241–248. Springer, Heidelberg (2006)
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Gu, H., Zhao, G., Zhang, J. (2010). MMSVC: An Efficient Unsupervised Learning Approach for Large-Scale Datasets. In: Li, K., Jia, L., Sun, X., Fei, M., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science(), vol 6330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15615-1_1
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DOI: https://doi.org/10.1007/978-3-642-15615-1_1
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