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Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces
Takashi Takenouchi · Takafumi Kanamori

Wed Dec 09 07:10 AM -- 07:35 AM (PST) @ Room 210 A

In this paper, we propose a novel parameter estimator for probabilistic models on discrete space. The proposed estimator is derived from minimization of homogeneous divergence and can be constructed without calculation of the normalization constant, which is frequently infeasible for models in the discrete space. We investigate statistical properties of the proposed estimator such as consistency and asymptotic normality, and reveal a relationship with the alpha-divergence. Small experiments show that the proposed estimator attains comparable performance to the MLE with drastically lower computational cost.

Author Information

Takashi Takenouchi (Future University Hakodate)
Takafumi Kanamori (Nagoya University)

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