Generating structure of latent variable models for nested data

Masakazu Ishihata NTT Communication Science Labo, Tomoharu Iwata NTT Communication Science Laboratories
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:401-410, 2014.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-labo14a, title = {Generating structure of latent variable models for nested data}, author = {Labo, Masakazu Ishihata NTT Communication Science and Laboratories, Tomoharu Iwata NTT Communication Science}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {401--410}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/labo14a/labo14a.pdf}, url = {https://proceedings.mlr.press/r12/labo14a.html}, abstract = {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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Generating structure of latent variable models for nested data %A Masakazu Ishihata NTT Communication Science Labo %A Tomoharu Iwata NTT Communication Science Laboratories %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-labo14a %I PMLR %P 401--410 %U https://proceedings.mlr.press/r12/labo14a.html %V R12 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Labo, M.I.N.C.S. & Laboratories, T.I.N.C.S.. (2014). Generating structure of latent variable models for nested data. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:401-410 Available from https://proceedings.mlr.press/r12/labo14a.html. Reissued by PMLR on 04 October 2026.

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