Abstract
We address the problem of unsupervised learning on graphs. The contribution is twofold: (1) we propose an EM algorithm for estimating the parameters of a mixture of radial densities on graphs on the basis of the graph orbifold framework; and (2) we compare orbifold-based clustering algorithms including the proposed EM algorithm against state-of-the-art methods based on pairwise dissimilarities. The results show that orbifold-based clustering methods complement the existing arsenal of clustering methods on graphs.
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Jain, B.J. (2013). Mixtures of Radial Densities for Clustering Graphs. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_13
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DOI: https://doi.org/10.1007/978-3-642-40261-6_13
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