Expectation propagation learning of a Dirichlet process mixture of Beta-Liouville distributions for proportional data clustering
- 1 August 2015
- journal article
- research article
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 43, 1-14
Abstract
No abstract availableKeywords
This publication has 65 references indexed in Scilit:
- Infinite Liouville mixture models with application to text and texture categorizationPattern Recognition Letters, 2012
- Hybrid Generative/Discriminative Approaches for Proportional Data Modeling and ClassificationIEEE Transactions on Knowledge and Data Engineering, 2011
- Bayesian hybrid generative discriminative learning based on finite Liouville mixture modelsPattern Recognition, 2011
- Facial Expression Recognition in JAFFE Dataset Based on Gaussian Process ClassificationIEEE Transactions on Neural Networks, 2010
- Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior2005
- A state-based technique for the summarization and recognition of gesture2002
- Robust Full Bayesian Learning for Radial Basis NetworksNeural Computation, 2001
- The recognition of human movement using temporal templatesIEEE Transactions on Pattern Analysis and Machine Intelligence, 2001
- The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow FieldsComputer Vision and Image Understanding, 1996
- Ferguson Distributions Via Polya Urn SchemesThe Annals of Statistics, 1973