Discovering Relevance-Dependent Bicluster Structure from Relational Data: A Model and Algorithm
- 1 November 2018
- journal article
- Published by Japanese Society for Artificial Intelligence in Transactions of the Japanese Society for Artificial Intelligence
- Vol. 33 (6), B-I46_1-I46_1
- https://doi.org/10.1527/tjsai.b-i46
Abstract
We propose a statistical model for relevance-dependent biclustering to analyze relational data. The proposed model factorizes relational data into bicluster structure with two features: (1) each object in a cluster has a relevance value, which indicates how strongly the object relates to the cluster and (2) all clusters are related to at least one dense block. These features simplify the task of understanding the meaning of each cluster because only a few highly relevant objects need to be inspected. We introduced the Relevance-Dependent Bernoulli Distribution (R-BD) as a prior for relevance-dependent binary matrices and proposed the novel Relevance-Dependent Infinite Biclustering (R-IB) model, which automatically estimates the number of clusters. Posterior inference can be performed efficiently using a collapsed Gibbs sampler because the parameters of the R-IB model can be fully marginalized out. Experimental results show that the R-IB extracts more essential bicluster structure with better computational efficiency than conventional models. We further observed that the biclustering results obtained by R-IB facilitate interpretation of the meaning of each cluster.Keywords
This publication has 19 references indexed in Scilit:
- Beyond Blocks: Hyperbolic Community DetectionLecture Notes in Computer Science, 2014
- An Extension of the Infinite Relational Model Incorporating Interaction between ObjectsLecture Notes in Computer Science, 2013
- Hierarchical Dirichlet ProcessesJournal of the American Statistical Association, 2006
- The Enron Corpus: A New Dataset for Email Classification ResearchLecture Notes in Computer Science, 2004
- Estimation and Prediction for Stochastic BlockstructuresJournal of the American Statistical Association, 2001
- Bayesian Density Estimation and Inference Using MixturesJournal of the American Statistical Association, 1995
- The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation ProblemJournal of the American Statistical Association, 1994
- Default ProbabilityCognitive Science, 1991
- Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric ProblemsThe Annals of Statistics, 1974
- Ferguson Distributions Via Polya Urn SchemesThe Annals of Statistics, 1973