The R Journal: accepted article

This article will be copy edited and may be changed before publication.

ider: Intrinsic Dimension Estimation with R PDF download
Hideitsu Hino

Abstract In many data analyses, the dimensionality of the observed data is high while its intrinsic dimension remains quite low. Estimating the intrinsic dimension of an observed dataset is an essential preliminary step for dimensionality reduction, manifold learning, and visualization. This paper introduces an R package, named ider, that implements eight intrinsic dimension estimation methods, including a recently proposed method based on second-order expansion of probability mass function and generalized linear model. The usage of each function in the package is explained with datasets generated using a function that is also included in the package.

Received: 2017-04-27; online 2017-11-05
CRAN packages: ider, ider, ider, fractal, nonlinearTseries, tseriesChaos, fractal, nonlinearTseries, tseriesChaos, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, Rcpp , CRAN Task Views implied by cited CRAN packages: TimeSeries, Finance, HighPerformanceComputing, NumericalMathematics


CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2017-054,
  author = {Hideitsu Hino},
  title = {{ider: Intrinsic Dimension Estimation with R}},
  year = {2017},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2017/RJ-2017-054/index.html}
}