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 |
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ページ(範囲) | 1838-1878 |
ページ数 | 41 |
ジャーナル | Neural Computation |
巻 | 29 |
号 | 7 |
DOI | |
出版ステータス | Published - 2017 7月 1 |
ASJC Scopus subject areas
- 人文科学(その他)
- 認知神経科学