DeepAI AI Chat
Log In Sign Up

EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering

10/03/2020
by   Lianmeng Jiao, et al.
nwpu.edu.cn
Université de Technologie de Compiègne
0

The Gaussian mixture model (GMM) provides a convenient yet principled framework for clustering, with properties suitable for statistical inference. In this paper, we propose a new model-based clustering algorithm, called EGMM (evidential GMM), in the theoretical framework of belief functions to better characterize cluster-membership uncertainty. With a mass function representing the cluster membership of each object, the evidential Gaussian mixture distribution composed of the components over the powerset of the desired clusters is proposed to model the entire dataset. The parameters in EGMM are estimated by a specially designed Expectation-Maximization (EM) algorithm. A validity index allowing automatic determination of the proper number of clusters is also provided. The proposed EGMM is as convenient as the classical GMM, but can generate a more informative evidential partition for the considered dataset. Experiments with synthetic and real datasets demonstrate the good performance of the proposed method as compared with some other prototype-based and model-based clustering techniques.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/03/2013

An Efficient Model Selection for Gaussian Mixture Model in a Bayesian Framework

In order to cluster or partition data, we often use Expectation-and-Maxi...
12/17/2020

Smoothed Gaussian Mixture Models for Video Classification and Recommendation

Cluster-and-aggregate techniques such as Vector of Locally Aggregated De...
12/07/2022

A parallelizable model-based approach for marginal and multivariate clustering

This paper develops a clustering method that takes advantage of the stur...
11/12/2012

A Comparative Study of Gaussian Mixture Model and Radial Basis Function for Voice Recognition

A comparative study of the application of Gaussian Mixture Model (GMM) a...
03/06/2017

Classification and clustering for samples of event time data using non-homogeneous Poisson process models

Data of the form of event times arise in various applications. A simple ...
09/29/2022

Likelihood adjusted semidefinite programs for clustering heterogeneous data

Clustering is a widely deployed unsupervised learning tool. Model-based ...
09/02/2020

An adequacy approach for deciding the number of clusters for OTRIMLE robust Gaussian mixture based clustering

We introduce a new approach to deciding the number of clusters. The appr...