Abstract
The Infinite Relational Model (IRM) introduced by Kemp et al. (Proc. AAAI2006) is one of the well-known probabilistic generative models for the co-clustering of relational data. The IRM describes the relationship among objects based on a stochastic block structure with infinitely many clusters. Although the IRM is flexible enough to learn a hidden structure with an unknown number of clusters, it sometimes fails to detect the structure if there is a large amount of noise or outliers. To overcome this problem, in this paper we propose an extension of the IRM by introducing a subset mechanism that selects a part of the data according to the interaction among objects. We also present posterior probabilities for running collapsed Gibbs sampling to learn the model from the given data. Finally, we ran experiments on synthetic and real-world datasets, and we showed that the proposed model is superior to the IRM in an environment with noise.
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References
Airoldi, E.M., Blei, D.M., Fienberg, S.E., Xing, E.P.: Mixed membership stochastic blockmodels. J. Mach. Learn. Res. 9, 1981–2014 (2008)
Aldous, D.: Exchangeability and related topics. In: Ecole d’Ete de Probabilities de Saint-Flour XIII, pp. 1–198 (1985)
Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: Proc. SIGKDD, pp. 269–274 (2001)
Dhillon, I.S., Mallela, S., Modha, D.S.: Information-theoretic co-clustering. In: Proc. SIGKDD, pp. 89–98 (2003)
Ding, C., Li, T., Peng, W., Park, H.: Orthogonal nonnegative matrix t-factorizations for clustering. In: Proc. SIGKDD, pp. 126–135 (2006)
Ferguson, T.S.: A bayesian analysis of some nonparametric problems. The Annals of Statistics 1(2), 209–230 (1973)
Fu, W., Song, L., Xing, E.P.: Dynamic mixed membership blockmodel for evolving networks. In: Proc. ICML, pp. 329–336 (2009)
Hoff, P.D.: Subset clustering of binary sequences, with an application to genomic abnormality data. Biometrics 61(4), 1027–1036 (2005)
Hubert, L., Arabie, P.: Comparing partitions. J. of Classification 2(1), 193–218 (1985)
Ishiguro, K., Ueda, N., Sawada, H.: Subset infinite relational models. J. Mach. Learn. Res. - Proceedings Track 22, 547–555 (2012)
Kemp, C., Tenenbaum, J.B., Griffiths, T.L., Yamada, T., Ueda, N.: Learning systems of concepts with an infinite relational model. In: Proc. AAAI, vol. 1, pp. 381–388 (2006)
Liu, J.S.: The collapsed gibbs sampler in bayesian computations with applications to a gene regulation problem. J. Am. Stat. Assoc. 89(427), 958–966 (1994)
Mørup, M., Schmidt, M.N., Hansen, L.K.: Infinite multiple membership relational modeling for complex networks. In: Proc. MLSP, pp. 1–6 (2011)
Osherson, D.N., Stern, J., Wilkie, O., Stob, M., Smith, E.E.: Default probability. Cognitive Science 15(2), 251–269 (1991)
Roy, D., Kemp, C., Mansinghka, V., Tenenbaum, J.: Learning annotated hierarchies from relational data. In: Proc. NIPS, pp. 1185–1192 (2006)
Shafiei, M.M., Milios, E.E.: Latent dirichlet co-clustering. In: Proc. ICDM, pp. 542–551 (2006)
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Ohama, I., Iida, H., Kida, T., Arimura, H. (2013). An Extension of the Infinite Relational Model Incorporating Interaction between Objects. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_13
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DOI: https://doi.org/10.1007/978-3-642-37456-2_13
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