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 a second-order expansion of a probability mass function and a 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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2017-054.zip
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
TimeSeries, Finance, HighPerformanceComputing, NumericalMathematics
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For attribution, please cite this work as
Hino, "ider: Intrinsic Dimension Estimation with R", The R Journal, 2017
BibTeX citation
@article{RJ-2017-054,
author = {Hino, Hideitsu},
title = {ider: Intrinsic Dimension Estimation with R},
journal = {The R Journal},
year = {2017},
note = {https://doi.org/10.32614/RJ-2017-054},
doi = {10.32614/RJ-2017-054},
volume = {9},
issue = {2},
issn = {2073-4859},
pages = {329-341}
}