The statistical analysis of compositional data
The statistical analysis of compositional data
Multivariate Liouville distributions
Journal of Multivariate Analysis
A new on-line learning algorithm for adaptive text filtering
Proceedings of the seventh international conference on Information and knowledge management
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Adaptive information filtering using evolutionary computation
Information Sciences: an International Journal - Special issue on frontiers in evolutionary algorithms
Bayesian online classifiers for text classification and filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Model Selection Criteria for Learning Belief Nets: An Empirical Comparison
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of Generative Models in Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Practical Bayesian estimation of a finite beta mixture through gibbs sampling and its applications
Statistics and Computing
Unsupervised Selection of a Finite Dirichlet Mixture Model: An MML-Based Approach
IEEE Transactions on Knowledge and Data Engineering
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Alternative prior assumptions for improving the performance of naïve Bayesian classifiers
Data Mining and Knowledge Discovery
A Dirichlet process mixture of generalized Dirichlet distributions for proportional data modeling
IEEE Transactions on Neural Networks
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Texture representations using subspace embeddings
Pattern Recognition Letters
Infinite Dirichlet mixture models learning via expectation propagation
Advances in Data Analysis and Classification
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This paper addresses the problem of proportional data modeling and clustering using mixture models, a problem of great interest and of importance for many practical pattern recognition, image processing, data mining and computer vision applications. Finite mixture models are broadly applicable to clustering problems. But, they involve the challenging problem of the selection of the number of clusters which requires a certain trade-off. The number of clusters must be sufficient to provide the discriminating capability between clusters required for a given application. Indeed, if too many clusters are employed overfitting problems may occur and if few are used we have a problem of underfitting. Here we approach the problem of modeling and clustering proportional data using infinite mixtures which have been shown to be an efficient alternative to finite mixtures by overcoming the concern regarding the selection of the optimal number of mixture components. In particular, we propose and discuss the consideration of infinite Liouville mixture model whose parameter values are fitted to the data through a principled Bayesian algorithm that we have developed and which allows uncertainty in the number of mixture components. Our experimental evaluation involves two challenging applications namely text classification and texture discrimination, and suggests that the proposed approach can be an excellent choice for proportional data modeling.