Uncertainty in Artificial Intelligence
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Transformation-based Probabilistic Clustering with Supervision
Siddharth Gopal, Yiming Yang
One of the common problems with clustering is that the generated clusters often do not match user expectations. This paper proposes a novel probabilistic framework that exploits supervised information in a discriminative and transferable manner to generate better clustering of unlabeled data. The supervision is provided by revealing the cluster assignments for some subset of the ground truth clusters and is used to learn a trans- formation of the data such that labeled instances form well-separated clusters with respect to the given clustering objective. This estimated trans- formation function enables us to fold the remain- ing unlabeled data into a space where new clus- ters hopefully match user expectations. While our framework is general, in this paper, we fo- cus on its application to Gaussian and von Mises- Fisher mixture models. Extensive testing on 23 data sets across several application domains re- vealed substantial improvement in performance over competing methods.
Pages: 270-279
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PDF Link: /papers/14/p270-gopal.pdf
AUTHOR = "Siddharth Gopal and Yiming Yang",
TITLE = "Transformation-based Probabilistic Clustering with Supervision",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "270--279"

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