A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Algorithm 548: Solution of the Assignment Problem [H]
ACM Transactions on Mathematical Software (TOMS)
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
The Journal of Machine Learning Research
Bayesian finite mixtures with an unknown number of components: The allocation sampler
Statistics and Computing
Block clustering with Bernoulli mixture models: Comparison of different approaches
Computational Statistics & Data Analysis
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Improved Bayesian inference for the stochastic block model with application to large networks
Computational Statistics & Data Analysis
| Hi-index | 0.00 |
We introduce a Bayesian extension of the latent block model for model-based block clustering of data matrices. Our approach considers a block model where block parameters may be integrated out. The result is a posterior defined over the number of clusters in rows and columns and cluster memberships. The number of row and column clusters need not be known in advance as these are sampled along with cluster memberhips using Markov chain Monte Carlo. This differs from existing work on latent block models, where the number of clusters is assumed known or is chosen using some information criteria. We analyze both simulated and real data to validate the technique.