Clustering

Some relevant publications:

• Heller, K.A., Williamson, S., and Ghahramani, Z. (2008) Statistical models for partial membership. Proceedings of the 25th International Conference on Machine Learning(ICML-2008).

• Heller, K.A., and Ghahramani, Z. (2007)
  A Nonparametric Bayesian Approach to Modeling Overlapping Clusters.
  In Eleventh International Conference on Artifical Intelligence and Statistics (AISTATS-2007). San Juan, Puerto Rico.

• Azran, A. and Ghahramani, Z. (2006)
  A New Approach to Data Driven Clustering.
  In International Conference on Machine Learning (ICML-2006).

• Azran, A. and Ghahramani, Z. (2006)
  Spectral methods for automatic multiscale data clustering.
  In IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2006).

• Ghahramani, Z. and Heller, K.A. (2006)
  Bayesian Sets.
  In Advances in Neural Information Processing Systems 18 (NIPS-2005).

• Heller, K.A., and Ghahramani, Z. (2005)
  Randomized Algorithms for Fast Bayesian Hierarchical Clustering. Statistics and Optimization of Clustering Workshop, Windsor, UK.

• Heller, K.A. and Ghahramani, Z. (2005)
  Bayesian Hierarchical Clustering, Gatsby Unit Technical Report GCNU-TR 2005-002. [ps] [pdf]
  A shorter version was published in the Twenty-second International Conference on Machine Learning (ICML-2005). [pdf]

• Zhu, X., Ghahramani, Z., and Lafferty, J. (2005)
  Time-Sensitive Dirichlet Process Mixture Models.
  Carnegie Mellon University Technical Report CMU-CALD-05-104.

• Zhang, J., Ghahramani, Z. and Yang, Y. (2005)
  A Probabilistic Model for Online Document Clustering with Application to Novelty Detection [ps]. [pdf]
  In Advances in Neural Information Processing Systems 17. (NIPS-2004)

• Minka, T.P., and Ghahramani, Z. (2003)
  Expectation Propagation for Infinite Mixtures.
  Technical Report, presented at the NIPS 2003 Workshop on Nonparametric Bayesian Methods and Infinite Models.
  Talk and abstract at this website.
• Ueda, N. and Ghahramani, Z. (2002)
  Bayesian model search for mixture models based on optimizing variational bounds
  Neural Networks 15: 1223-1241.

• Ghahramani, Z. and Beal, M.J. (1999)
  Variational inference for Bayesian mixtures of factor analysers [pdf] [ps.gz] [abstract]
  In Neural Information Processing Systems 12

• Ueda, N., Nakano, R., Ghahramani, Z., and Hinton, G. E. (1999)
  SMEM Algorithm for Mixture Models. [pdf] [ps]
  In Neural Computation.

• Ghahramani, Z. and Hinton, G.E. (1996)
  The EM Algorithm for Mixtures of Factor Analyzers
  University of Toronto Technical Report CRG-TR-96-1, 8 pages (short note).
  Software written in Matlab.