Work

Bayesian Model Averaging with Diffused Priors for Model-Based Clustering Under a Cluster Forests Architecture

Year: 2025

Type: article

Abstract: This paper considers a class of generative graphical models for parsimonious modeling of Gaussian mixtures and robust unsupervised learning, each assuming that the data are generated independently and identically from a finite mixture model with an extended naïve Bayes structure. To account for model uncertainty, the expectation model-averaging algorithm, which approximates the Bayesian model averaging with incomplete data, is introduced using a novel class of non-informative priors for the parameters. A Cluster Forests architecture to circumvent intractable model averaging over a large selective model space is developed. Extensive synthetic data experiments and real-world data applications show that the proposed methodology can produce clustering results of high robustness and attain good model detection performance.

Source: Symmetry

DOI: 10.3390/sym17111879

Authors: Shan Feng, Wenxian Xie, Yufeng Nie

Institution: Northwestern Polytechnical University

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China