@inproceedings{c385c65fcf8a44a6bbdcc599dee8235f,
title = "On a Convergence Property of a Geometrical Algorithm for Statistical Manifolds",
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.",
keywords = "Dimension reduction, Information geometry, Mixture model, Pythagorean theorem",
author = "Shotaro Akaho and Hideitsu Hino and Noboru Murata",
note = "Funding Information: Supported by JSPS KAKENHI Grant Number 17H01793, 19K12111. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 26th International Conference on Neural Information Processing, ICONIP 2019 ; Conference date: 12-12-2019 Through 15-12-2019",
year = "2019",
doi = "10.1007/978-3-030-36802-9_29",
language = "English",
isbn = "9783030368012",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "262--272",
editor = "Tom Gedeon and Wong, {Kok Wai} and Minho Lee",
booktitle = "Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings",
}