Uncertainty in Artificial Intelligence
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Generating structure of latent variable models for nested data
Masakazu Ishihata, Tomoharu Iwata
Probabilistic latent variable models have been successfully used to capture intrinsic character- istics of various data. However, it is nontrivial to design appropriate models for given data because it requires both machine learning and domain- specific knowledge. In this paper, we focus on data with nested structure and propose a method to automatically generate a latent variable model for the given nested data, with the proposed method, the model structure is adjustable by its structural parameters. Our model can represent a wide class of hierarchical and sequential la- tent variable models including mixture models, latent Dirichlet allocation, hidden Markov mod- els and their combinations in multiple layers of the hierarchy. Even when deeply-nested data are given, where designing a proper model is diffi- cult even for experts, our method generate an ap- propriate model by extracting the essential infor- mation. We present an efficient variational in- ference method for our model based on dynamic programming on the given data structure. We ex- perimentally show that our method generates cor- rect models from artificial datasets and demon- strate that models generated by our method can extract hidden structures of blog and news article datasets.
Pages: 350-359
PS Link:
PDF Link: /papers/14/p350-ishihata.pdf
AUTHOR = "Masakazu Ishihata and Tomoharu Iwata",
TITLE = "Generating structure of latent variable models for nested data",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "350--359"

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