Latent Dirichlet Bayesian Co-Clustering
Abstract
Co-clustering has emerged as an important technique for mining contingency data matrices. However, almost all existing co-clustering algorithms are hard partitioning, assigning each row and column of the data matrix to one cluster. Recently a Bayesian co-clustering approach has been proposed which allows a probability distribution membership in row and column clusters. The approach uses variational inference for parameter estimation. In this work, we modify the Bayesian co-clustering model, and use collapsed Gibbs sampling and collapsed variational inference for parameter estimation. Our empirical evaluation on real data sets shows that both collapsed Gibbs sampling and collapsed variational inference are able to find more accurate likelihood estimates than the standard variational Bayesian co-clustering approach.
Cite
Text
Wang et al. "Latent Dirichlet Bayesian Co-Clustering." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009. doi:10.1007/978-3-642-04174-7_34Markdown
[Wang et al. "Latent Dirichlet Bayesian Co-Clustering." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009.](https://mlanthology.org/ecmlpkdd/2009/wang2009ecmlpkdd-latent/) doi:10.1007/978-3-642-04174-7_34BibTeX
@inproceedings{wang2009ecmlpkdd-latent,
title = {{Latent Dirichlet Bayesian Co-Clustering}},
author = {Wang, Pu and Domeniconi, Carlotta and Laskey, Kathryn B.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2009},
pages = {522-537},
doi = {10.1007/978-3-642-04174-7_34},
url = {https://mlanthology.org/ecmlpkdd/2009/wang2009ecmlpkdd-latent/}
}