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On a Convergence Property of a Geometrical Algorithm for Statistical Manifolds

  • Conference paper
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Neural Information Processing (ICONIP 2019)

Abstract

In this paper, we examine a geometrical projection algorithm for statistical inference. The algorithm is based on Pythagorean relation and it is derivative-free as well as representation-free that is useful in nonparametric cases. We derive a bound of learning rate to guarantee local convergence. In special cases of m-mixture and e-mixture estimation problems, we calculate specific forms of the bound that can be used easily in practice.

Supported by JSPS KAKENHI Grant Number 17H01793, 19K12111.

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Notes

  1. 1.

    Details of the proof will appear in the full version of the paper [3].

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Correspondence to Shotaro Akaho .

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Akaho, S., Hino, H., Murata, N. (2019). On a Convergence Property of a Geometrical Algorithm for Statistical Manifolds. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_29

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  • DOI: https://doi.org/10.1007/978-3-030-36802-9_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36801-2

  • Online ISBN: 978-3-030-36802-9

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