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http://hdl.handle.net/1783.1/780
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| Title: | Hierarchical latent class models for cluster analysis |
| Authors: | Zhang, Nevin Lianwen |
| Keywords: | Cluster analysis
Latent class models
Local dependence
Search-based algorithm
Hierarchical latent class models |
| Issue Date: | 2002 |
| Citation: | To be published in Journal of Machine Learning Research |
| Series/Report no.: | Computer Science Technical Report ; HKUST-CS02-02 |
| Abstract: | 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. |
| URI: | http://hdl.handle.net/1783.1/780 |
| Appears in Collections: | CSE Technical Reports
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| aaai02.pdf | | 199Kb | Adobe PDF | View/Open |
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