DeepAI
Log In Sign Up

Hierarchical Infinite Relational Model

08/16/2021
by   Feras A. Saad, et al.
0

This paper describes the hierarchical infinite relational model (HIRM), a new probabilistic generative model for noisy, sparse, and heterogeneous relational data. Given a set of relations defined over a collection of domains, the model first infers multiple non-overlapping clusters of relations using a top-level Chinese restaurant process. Within each cluster of relations, a Dirichlet process mixture is then used to partition the domain entities and model the probability distribution of relation values. The HIRM generalizes the standard infinite relational model and can be used for a variety of data analysis tasks including dependence detection, clustering, and density estimation. We present new algorithms for fully Bayesian posterior inference via Gibbs sampling. We illustrate the efficacy of the method on a density estimation benchmark of twenty object-attribute datasets with up to 18 million cells and use it to discover relational structure in real-world datasets from politics and genomics.

READ FULL TEXT

page 3

page 7

page 8

06/27/2012

Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation

Nonparametric Bayesian approaches to clustering, information retrieval, ...
12/03/2015

CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data

There is a widespread need for statistical methods that can analyze high...
01/25/2015

Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction

A hierarchical gamma process infinite edge partition model is proposed t...
05/13/2019

Bayesian Hierarchical Mixture Clustering using Multilevel Hierarchical Dirichlet Processes

This paper focuses on the problem of hierarchical non-overlapping cluste...
09/15/2015

Macau: Scalable Bayesian Multi-relational Factorization with Side Information using MCMC

We propose Macau, a powerful and flexible Bayesian factorization method ...
10/09/2018

Unsupervised Object Matching for Relational Data

We propose an unsupervised object matching method for relational data, w...
07/04/2020

Deep Graph Random Process for Relational-Thinking-Based Speech Recognition

Lying at the core of human intelligence, relational thinking is characte...