“Dimensionality reduction” (DR) is a widely used approach to find low dimensional and interpretable representations of data that are natively embedded in high-dimensional spaces. DR can be realized by a plethora of methods with different properties, objectives, and, hence, (dis)advantages. The resulting low-dimensional data embeddings are often difficult to compare with objective criteria. Here, we introduce the dimRed and coRanking packages for the R language. These open source software packages enable users to easily access multiple classical and advanced DR methods using a common interface. The packages also provide quality indicators for the embeddings and easy visualization of high dimensional data. The coRanking package provides the functionality for assessing DR methods in the co-ranking matrix framework. In tandem, these packages allow for uncovering complex structures high dimensional data. Currently 15 DR methods are available in the package, some of which were not previously available to R users. Here, we outline the dimRed and coRanking packages and make the implemented methods understandable to the interested reader.
dimRed, coRanking, kernlab, vegan, RANN, igraph, lle, diffusionMap, MASS, igraph, Rtsne, fastICA, DRR
Multivariate, Optimization, Psychometrics, Spatial, Environmetrics, gR, Graphics, ChemPhys, Cluster, Distributions, Econometrics, MachineLearning, NaturalLanguageProcessing, NumericalMathematics, Phylogenetics, Robust, SocialSciences
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For attribution, please cite this work as
Kraemer, et al., "dimRed and coRanking - Unifying Dimensionality Reduction in R", The R Journal, 2018
BibTeX citation
@article{RJ-2018-039,
author = {Kraemer, Guido and Reichstein, Markus and Mahecha, Miguel D.},
title = {dimRed and coRanking - Unifying Dimensionality Reduction in R},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-039},
doi = {10.32614/RJ-2018-039},
volume = {10},
issue = {1},
issn = {2073-4859},
pages = {342-358}
}