2002/03/01 by Mário A. T. Figueiredo, Anil K. Jain · 3 citations
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Machine Learning and Algorithms #Target Tracking and Data Fusion in Sensor Networks #Mixture model #Expectation–maximization algorithm #Initialization #Computer science #Model selection #Unsupervised learning #Artificial intelligence #Convergence (economics) #Gaussian process #Parametric statistics #Algorithm #Pattern recognition (psychology) #Gaussian #Mathematics #Maximum likelihood
paper · doi:10.1109/34.990138
openalex publication_date 2002/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03
This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective "unsupervised" is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectation-maximization (EM) algorithm, it does not require careful initialization. The proposed method also avoids another drawback of EM for mixture fitting: the possibility of convergence toward a singular estimate at the boundary of the parameter space. The novelty of our approach is that we do not use a model selection criterion to choose one among a set of preestimated candidate models; instead, we seamlessly integrate estimation and model selection in a single algorithm. Our technique can be applied to any type of parametric mixture model for which it is possible to write an EM algorithm; in this paper, we illustrate it with experiments involving Gaussian mixtures. These experiments testify for the good performance of our approach.