Abstract
For most kernel-based clustering algorithms, their performance will heavily hinge on the choice of kernel. In this paper, we propose a novel kernel learning algorithm within the framework of the Local Learning based Clustering (LLC) (Wu & Schölkopf 2006). Given multiple kernels, we associate a non-negative weight with each Hilbert space for the corresponding kernel, and then extend our previous work on feature selection (Zeng & Cheung 2009) to select the suitable Hilbert spaces for LLC. We show that it naturally renders a linear combination of kernels. Accordingly, the kernel weights are estimated iteratively with the local learning based clustering. The experimental results demonstrate the effectiveness of the proposed algorithm on the benchmark document datasets.
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References
Wu, M., Schölkopf, B.: A Local Learning Approach for Clustering. In: NIPS, pp. 1529–1536 (2006)
Zeng, H., Cheung, Y.M.: Feature Selection for Local Learning based Clustering. In: PAKDD, pp. 414–425 (2009)
Scholköpf, B., Smola, A.J.: Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (2002)
Zelnik-Manor, L., Perona, P.: Self-tuning Spectral Clustering. In: NIPS, pp. 1601–1608 (2004)
Bach, F.R., Jordan, M.I.: Learning Spectral Clustering, with Application to Speech Separation. JMLR 7, 1963–2001 (2006)
Lanckriet, G.R.G., Cristianini, N., Bartlett, P., Ghaoui, M.I.E., Jordan, M.I.: Learning the Kernel Matrix with Semidefinite Programming. JMLR 5, 27–72 (2004)
Valizadegan, H., Jin, R.: Generalized Maximum Margin Clustering and Unsupervised Kernel Learning. In: NIPS, pp. 1417–1424 (2007)
Lange, T., Buhmann, J.: Fusion of Similarity Data in Clustering. In: NIPS, pp. 723–730 (2005)
Ng, A., Jordan, M., Weiss, Y.: On Spectral Clustering: Analysis and an Algorithm. In: NIPS, pp. 849–856 (2001)
Yu, S.X., Shi, J.: Multiclass Spectral Clustering. In: ICCV, pp. 313–319 (2003)
Calamai, P.H., Moré, J.J.: Projected Gradients Methods for Linearly Constrained Problems. Math. Prog., 93–116 (1987)
Rakotomamonjy, A., Bach, F., Canu, S., Grandvalet, Y.: More Efficiency in Multiple Kernel Learning. In: ICML, pp. 775–782 (2007)
Bonnans, J.F., Sharpiro, A.: Perturbation Analysis of Optimization Problems (2000)
Chapelle, O., Vanpnik, V., Bousquet, O., Mukherjee, S.: Choosing Multiple Parameters for Surport Vector Machines. Mach. Learn., 131–159 (2002)
Karypis, G.: CLUTO-A Clustering Toolkit (2002), http://www-users.cs.umn.edu/~karypis/cluto
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Zeng, H., Cheung, Ym. (2009). Kernel Learning for Local Learning Based Clustering. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_2
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DOI: https://doi.org/10.1007/978-3-642-04274-4_2
Publisher Name: Springer, Berlin, Heidelberg
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