Latent variable models and factors analysis
Latent variable models and factors analysis
Elements of information theory
Elements of information theory
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Algorithms for Model-Based Gaussian Hierarchical Clustering
SIAM Journal on Scientific Computing
A structural EM algorithm for phylogenetic inference
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Learning the Dimensionality of Hidden Variables
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Learning with mixtures of trees
Learning with mixtures of trees
Latent Structure Models for the Analysis of Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Latent tree models and diagnosis in traditional Chinese medicine
Artificial Intelligence in Medicine
Discovering Latent Structures: Experience with the CoIL Challenge 2000 Data Set
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Effective dimensions of hierarchical latent class models
Journal of Artificial Intelligence Research
Effective dimensions of partially observed polytrees
International Journal of Approximate Reasoning
Dimension correction for hierarchical latent class models
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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Latent class models are used for cluster analysis of categorical data. Underlying such a model is the assumption that the observed variables are mutually independent given the class variable. A serious problem with the use of latent class models, known as local dependence, is that this assumption is often untrue. In this paper we propose hierarchical latent class models as a framework where the local dependence problem can be addressed in a principled manner. We develop a search-based algorithm for learning hierarchical latent class models from data. The algorithm is evaluated using both synthetic and real-world data.