Abstract
We focus on spherical shells clustering by a mini-max information (MMI) clustering algorithm based on mini-max optimization of mutual information (MI). The minimization optimization leads to a mass constrained deterministic annealing (DA) approach, which is independent of the choice of the initial data configuration and has the ability to avoid poor local optima. The maximization optimization provides a robust estimation of probability soft margin to phase out outliers. Furthermore, a novel cluster validity criteria is estimated to determine an optimal cluster number of spherical shells for a given set of data. The effectiveness of MMI algorithm for clustering spherical shells is demonstrated by experimental results.
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© 2006 Springer-Verlag Berlin Heidelberg
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Yang, X., Song, Q., Zhang, W., Wang, Z. (2006). Clustering Spherical Shells by a Mini-Max Information Algorithm. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_23
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DOI: https://doi.org/10.1007/11612704_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31244-4
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