Results 261 to 270 of about 11,564,802 (285)

Online Variational Learning for a Dirichlet Process Mixture of Dirichlet Distributions and its Application

2012 11th International Conference on Machine Learning and Applications, 2012
Online algorithms allow data points to be processed sequentially, which is important for real-time applications. In this paper, we propose a novel online clustering approach based on a mixture of Dirichlet processes with Dirichlet distributions, which can be viewed as an extension of the finite Dirichlet mixture model to the infinite case. Our approach
Wentao Fan 0001, Nizar Bouguila
openaire   +1 more source

Variational Learning of Dirichlet Process Mixtures of Generalized Dirichlet Distributions and Its Applications

2012
In this paper, we introduce a nonparametric Bayesian approach for clustering based on both Dirichlet processes and generalized Dirichlet (GD) distribution. Thanks to the proposed approach, the obstacle of estimating the correct number of clusters is sidestepped by assuming an infinite number of components.
Wentao Fan 0001, Nizar Bouguila
openaire   +1 more source

Distance Dependent Maximum Margin Dirichlet Process Mixture

2019
We propose distance dependent maximum margin Dirichlet Process Mixture (STANDPM), a nonparametric Bayesian clustering model that combines distance-based priors with the discriminatively learned likelihood of the Maximum Margin Dirichlet Process Mixture. STANDPM generalizes the distance-based prior introduced in the distance dependent Chinese Restaurant
Quan Nguyen, Mikko Lauri, Simone Frintrop   +2 more
openaire   +2 more sources

Learning Label Distribution with Dirichlet Process Mixture Model

Proceedings of the AAAI Conference on Artificial Intelligence
Label Distribution Learning (LDL) is an effective machine learning paradigm for addressing label ambiguity, where each sample is annotated with a distribution that conveys rich semantic information. However, during the actual annotation process of label distributions, annotators often exhibit divergent labeling preferences for the same sample.
Minglong Wang, Weiwei Li 0001, Yunan Lu 0002, Xiuyi Jia   +3 more
openaire   +2 more sources

Dirichlet process mixtures and extensions

2023
Michael J. Daniels, Antonio Linero, Jason Roy   +2 more
openaire   +1 more source

Dirichlet Process Mixture Model for Summarizing the Social Web

2015
Automatic summarizations have gained increasing attentions as they not only improve reading experiences but also facilitate management of collective knowledge on the social web. The social web is featured by social interactions. Ignoring this type of information limits the ability of traditional summarization techniques to generate more intelligent and
Xinjun Guan, Ying Yang, Xinru Yang, Chen Lin 0001   +3 more
openaire   +1 more source

Dirichlet Process Mixture of Generalized Inverted Dirichlet Distributions for Positive Vector Data With Extended Variational Inference

IEEE Transactions on Neural Networks and Learning Systems, 2022
Jun Guo, Jingyi Yu, Deyu Meng
exaly  

Empirical Bayes Estimation of a Binomial Parameter via Mixtures of Dirichlet Processes

Annals of Statistics, 1979
Ronald Christensen
exaly  

Marginal Likelihood and Bayes Factors for Dirichlet Process Mixture Models

Journal of the American Statistical Association, 2003
Siddhartha Chib
exaly  

Variational Bayesian Learning for Dirichlet Process Mixture of Inverted Dirichlet Distributions in Non-Gaussian Image Feature Modeling

IEEE Transactions on Neural Networks and Learning Systems, 2019
Yi-Zhe Song, W Kleijn, Zhanyu Ma
exaly