Abstract
Clustering techniques demand on suitable models of data structures to infer the main samples patterns. Nonetheless, detection of data structures becomes a difficult task when dealing with nonlinear data relationships and complex distributions. Here, to support clustering tasks, we introduce a new graph building strategy based on a compactly supported kernel technique. Thus, our approach makes relevant pair-wise sample relationships by finding a sparse kernel matrix that codes the main sample connections. Clustering performance is assessed on synthetic and real-world data sets. Obtained results show that the proposed method enhances the data interpretability and separability by revealing relevant data relationships into a graph-based representation.
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Castro-Ospina, A.E., Álvarez-Meza, A.M., Castellanos-Domínguez, C.G.: Automatic graph building approach for spectral clustering. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013, Part I. LNCS, vol. 8258, pp. 190–197. Springer, Heidelberg (2013)
Cortes, C., Mohri, M., Rostamizadeh, A.: Algorithms for learning kernels based on centered alignment. The Journal of Machine Learning Research 13, 795–828 (2012)
Filippone, M., Camastra, F., Masulli, F., Rovetta, S.: A survey of kernel and spectral methods for clustering. Pattern Recognition 41(1), 176–190 (2008)
Liping, C., Xuchuan, Z., Jiancheng, S.: The approach of adaptive spectral clustering analyze on high dimensional data. In: ICCIS, pp. 160–162 (2010)
Liu, W., Principe, J.C., Haykin, S.: Kernel Adaptive Filtering: A Comprehensive Introduction, vol. 57. John Wiley & Sons (2011)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, California, USA, vol. 1, p. 14 (1967)
Perona, P., Zelnik-Manor, L.: Self-tuning spectral clustering. Advances in Neural Information Processing Systems 17, 1601–1608 (2004)
Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward objective evaluation of image segmentation algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 929–944 (2007)
Von Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17(4), 395–416 (2007)
Zhang, H., Genton, M., Liu, P.: Compactly supported radial basis function kernels (2004), http://www4.stat.ncsu.edu/hzhang/research.html
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Álvarez-Meza, A.M., Castro-Ospina, A.E., Castellanos-Dominguez, G. (2014). Spectral Clustering Using Compactly Supported Graph Building. In: Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_40
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DOI: https://doi.org/10.1007/978-3-319-12568-8_40
Publisher Name: Springer, Cham
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