Abstract
A clustering algorithm for GPDF data called Centroid Neural Network with Bhattacharyya Kernel (BK-CNN) is proposed in this paper. The proposed BK-CNN is based on the unsupervised competitive centroid neural network (CNN) and employs a kernel method for data projection. In order to cluster the GPDF data, the Bhattacharyya kernel is used to measure the distance between two probability distributions for data projection. When applied to GPDF data in an image classification model, the experiment results show that the proposed BK-CNN algorithm is more efficient than other conventional algorithms such as k-means algorithm, SOM and CNN with Bhattacharyya distance.
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References
Hartigan, J.: Clustering Algorithms. Wiley, New York (1975)
Kohonen, T.: The Self-Organizing Map. Proceedings of the IEEE 78, 1464–1480 (1990)
Park, D.C.: Centroid Neural Network for Unsupervised Competitive Learning. IEEE Transaction on Neural Networks 11, 520–528 (2000)
Gokcay, E., Principe, J.C.: Information theoretic clustering. IEEE Transaction on Pattern Analysis and Machine Intelligence 24, 158–171 (2002)
Park, D.C., Oh, H.K., Min, S.S.: Clustering of Gaussian Probability Density Functions Using Centroid Neural Networks. IEE Electronic Letters 49, 381–382 (2003)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machine. Cambridge Univ. Press, Cambridge (2000)
Chen, S., Zhang, D.: Robust Image Segmentation using FCM with Spatial Constraints Based on New Kernel-Induced Distance Measure. IEEE Trans. on Systems Man and Cybernetics 43, 1907–1916 (2004)
Jebra, T., Kondor, R.: Bhattacharyya and Expected Likelihood Kernels. In: Proc. COLT (2003)
Muller, K.R., et al.: An Introduction to Kernel-Based Learning Algorithms. IEEE Transactions on Neural Networks 12, 181–201 (2001)
Cover, T.M.: Geomeasureal and Statistical Properties of Systems of Linear Inequalities in Pattern Recognition. Electron. Computing 14, 326–334 (1965)
Girolami, M.: Mercer Kernel-Based Clustering in Feature Space. IEEE Trans. on Neual Networks 13, 780–784 (2002)
Chen, J.H., Chen, C.S.: Fuzzy Kernel Perceptron. IEEE Trans. on Neural Networks 13, 1364–1373 (2002)
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Lee, SJ., Park, DC. (2007). Centroid Neural Network with Bhattacharyya Kernel for GPDF Data Clustering. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_65
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DOI: https://doi.org/10.1007/978-3-540-71701-0_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71700-3
Online ISBN: 978-3-540-71701-0
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