Local intrinsic dimension estimation by generalized linear Modeling

Hideitsu Hino, Jun Fujiki, Shotaro Akaho, Noboru Murata

Research output: Contribution to journalLetterpeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1838-1878
Number of pages41
JournalNeural Computation
Volume29
Issue number7
DOIs
Publication statusPublished - 2017 Jul 1

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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