Big Data Science

Automatic tailor-made data analysis

- Generating probabilistic models using structure information -

Abstract

Probabilistic latent variable models have successfully captured the intrinsic characteristics of various data. Understanding them is helpful for discovering latent rules and facts behind data. However, it is nontrivial to design appropriate models for given data because both machine learning and domain-specific knowledge are required.
We propose an automatic model generation method for data with hierarchical structure. Our method constructs an appropriate model for given data by extracting important hierarchies and preserves hierarchical and sequential information if needed or desired. We automatically extract latent structures of given data and discover hidden rules and facts behind the data.

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Poster


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Presenters

Masakazu Ishihata
Masakazu Ishihata
Innovation Communication Laboratory
Tomoharu Iwata
Tomoharu Iwata
Innovation Communication Laboratory
Katsuhiko Ishiguro
Katsuhiko Ishiguro
Innovation Communication Laboratory
Kou Takeuchi
Kou Takeuchi
Innovation Communication Laboratory