The statistical analysis of compositional data
The statistical analysis of compositional data
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Convergence assessment techniques for Markov chain Monte Carlo
Statistics and Computing
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning a multivariate Gaussian mixture model with the reversible jump MCMC algorithm
Statistics and Computing
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Modeling word burstiness using the Dirichlet distribution
ICML '05 Proceedings of the 22nd international conference on Machine learning
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Unsupervised Selection of a Finite Dirichlet Mixture Model: An MML-Based Approach
IEEE Transactions on Knowledge and Data Engineering
Dirichlet aggregation: unsupervised learning towards an optimal metric for proportional data
Proceedings of the 24th international conference on Machine learning
Evaluating bag-of-visual-words representations in scene classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A Graphical Model for Context-Aware Visual Content Recommendation
IEEE Transactions on Multimedia
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
The infinite Student's t-mixture for robust modeling
Signal Processing
Infinite Liouville mixture models with application to text and texture categorization
Pattern Recognition Letters
An infinite mixture of inverted dirichlet distributions
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Spatial color image segmentation based on finite non-Gaussian mixture models
Expert Systems with Applications: An International Journal
Journal of Visual Communication and Image Representation
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
Finite asymmetric generalized Gaussian mixture models learning for infrared object detection
Computer Vision and Image Understanding
Infinite Dirichlet mixture models learning via expectation propagation
Advances in Data Analysis and Classification
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In this paper, we propose a clustering algorithm based on both Dirichlet processes and generalized Dirichlet distribution which has been shown to be very flexible for proportional data modeling. Our approach can be viewed as an extension of the finite generalized Dirichlet mixture model to the infinite case. The extension is based on nonparametric Bayesian analysis. This clustering algorithm does not require the specification of the number of mixture components to be given in advance and estimates it in a principled manner. Our approach is Bayesian and relies on the estimation of the posterior distribution of clusterings using Gibbs sampler. Through some applications involving real-data classification and image databases categorization using visual words, we show that clustering via infinite mixture models offers a more powerful and robust performance than classic finite mixtures.