Ueda, Naonori (NTT Communication Science Lab.)

Bayesian Probabilistic Models for Data Partitioning and Their Applications

Scheduled for presentation during the Mini-Symposium "Randomized and Probabilistic Techniques for Complex Systems Design" (MoP08), Monday, July 24, 2006, 16:10−16:35, Room I

17th International Symposium on Mathematical Theory of Networks and Systems, July 24-28, 2006, Kyoto, Japan

This information is tentative and subject to change. Compiled on April 20, 2024

Abstract

This paper reviews Bayesian statistical generative models for data partitioning for complex data analysis. The Bayesian modeling gives us a principled approach for clustering a set of complex data into an unknown number of disjoint or overlapped data each of which can be represented by some simple distribution. This paper also introduces nonparametric Bayesian approach (Dirichlet process mixture models), which enable us to define distributions over the countably infinite sets that faces with the partitioning problems. Some real appications including document clustering/classification and ontology learning have also been presented to show the usefulness of the modeling.