Abstract
Mixture modeling is one of the simplest ways to represent complicated probability density functions, and to integrate information from different sources. There are two typical mixtures in the context of information geometry, the m- and e-mixtures. This paper proposes a novel framework of non-parametric e-mixture modeling by using a simple estimation algorithm based on geometrical insights into the characteristics of the e-mixture. An experimental result supports the proposed framework.
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Acknowledgement
Part of this work was supported by JSPS KAKENHI No. 25120009, 25120011, and 16K16108.
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Hino, H., Takano, K., Akaho, S., Murata, N. (2016). Non-parametric e-mixture of Density Functions. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_1
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DOI: https://doi.org/10.1007/978-3-319-46672-9_1
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