Local intrinsic dimension estimation by generalized linear Modeling

Hideitsu Hino, Jun Fujiki, Shotaro Akaho, Noboru Murata

研究成果: Letter査読

2 被引用数 (Scopus)

抄録

We propose a method for intrinsic dimension estimation. By fitting the power of distance from an inspection point and the number of samples included inside a ball with a radius equal to the distance, to a regression model, we estimate the goodness of fit. Then, by using the maximum likelihood method, we estimate the local intrinsic dimension around the inspection point. The proposed method is shown to be comparable to conventional methods in global intrinsic dimension estimation experiments. Furthermore, we experimentally show that the proposed method outperforms a conventional local dimension estimation method.

本文言語English
ページ(範囲)1838-1878
ページ数41
ジャーナルNeural Computation
29
7
DOI
出版ステータスPublished - 2017 7 1

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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