We present a probabilistic block-constant biclustering model that simultaneously clusters
\nrows and columns of a data matrix. All entries with the same row cluster and column cluster
\nform a bicluster. Each cluster is part of a mixture having a nonparametric Bayesian prior. The
\nnumber of biclusters is therefore treated as a nuisance parameter and is implicitly integrated
\nover during simulation. Missing entries are completely integrated out of the model, allowing
\nus to completely bipass the common requirement for biclustering algorithms that missing
\nvalues be filled before analysis, but also makes it robust to high rates of missing values. By
\nusing a Gaussian model for the density of entries in bliclusters, an efficient sampling algorithm
\nis produced because bicluster parameters are analytically integrated out. We present
\nseveral inference procedures for sampling cluster indicators, including Gibbs and split-merge
\nmoves. We show that our method is competitive, if not superior, to existing imputation methods,
\nespecially for high missing rates, despite imputing constant values for entire blocks of
\ndata. We present imputation experiments and exploratory biclustering results.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present a probabilistic block-constant biclustering model that simultaneously clusters rows and columns of a data matrix. All entries with the same row cluster and column cluster form a bicluster. Each cluster is part of a mixture having a nonparametric Bayesian prior. The number of biclusters is therefore treated as a nuisance parameter and is […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193718],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-443817","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"Department of Computer Science, University of Toronto","msr_edition":"UTML TR 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