An Introduction to Variational Methods for Graphical Models
Machine Learning
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust mixture modelling using the t distribution
Statistics and Computing
Approximate Bayes Factors for Image Segmentation: The Pseudolikelihood Information Criterion (PLIC)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Speech Recognition Based on Student's t-Distribution Derived from Total Bayesian Framework
IEICE - Transactions on Information and Systems
The Variational Bayes Method in Signal Processing (Signals and Communication Technology)
The Variational Bayes Method in Signal Processing (Signals and Communication Technology)
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust probabilistic PCA with missing data and contribution analysis for outlier detection
Computational Statistics & Data Analysis
The mixtures of Student's t-distributions as a robust framework for rigid registration
Image and Vision Computing
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
On EM Estimation for Mixture of Multivariate t-Distributions
Neural Processing Letters
Constrained monotone EM algorithms for mixtures of multivariate t distributions
Statistics and Computing
Robust Bayesian mixture modelling
Neurocomputing
A Dirichlet process mixture of generalized Dirichlet distributions for proportional data modeling
IEEE Transactions on Neural Networks
The infinite hidden Markov random field model
IEEE Transactions on Neural Networks
An extension of the standard mixture model for image segmentation
IEEE Transactions on Neural Networks
Variational learning for Gaussian mixture models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
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Finite mixture models have been widely used for modeling probability distribution of real data sets due to its benefits from analytical tractability. Among the finite mixtures, the finite Student's t-mixture model (SMM) are tolerant to the untypical data (outliers). However, the SMM could not automatically determine the proper number of components, which is important and may has a significant effect on the learned model. In this paper, we propose an infinite Student's t-mixture model (iSMM) to handle this issue. This model is based on the Dirichlet process mixture which assumes the number of components in a mixture is infinite in advance, and determines the appropriate value of this number according to the observed data. Moreover, we derive an efficient variational Bayesian inference algorithm for the proposed model. Through applications in blind signal detection and image segmentation, it is shown that the iSMM possesses the advantages of both the Student's t-distribution and the Dirichlet process mixture, offering a more powerful and robust performance than competing models.