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cff-version: 1.2.0
message: 'To cite package "lolR" in publications use:'
type: software
license: GPL-2.0-only
title: 'lolR: Linear Optimal Low-Rank Projection'
version: '2.1'
doi: 10.32614/CRAN.package.lolR
abstract: Supervised learning techniques designed for the situation when the dimensionality
  exceeds the sample size have a tendency to overfit as the dimensionality of the
  data increases. To remedy this High dimensionality; low sample size (HDLSS) situation,
  we attempt to learn a lower-dimensional representation of the data before learning
  a classifier. That is, we project the data to a situation where the dimensionality
  is more manageable, and then are able to better apply standard classification or
  clustering techniques since we will have fewer dimensions to overfit. A number of
  previous works have focused on how to strategically reduce dimensionality in the
  unsupervised case, yet in the supervised HDLSS regime, few works have attempted
  to devise dimensionality reduction techniques that leverage the labels associated
  with the data. In this package and the associated manuscript Vogelstein et al. (2017)
  <arXiv:1709.01233>, we provide several methods for feature extraction, some utilizing
  labels and some not, along with easily extensible utilities to simplify cross-validative
  efforts to identify the best feature extraction method. Additionally, we include
  a series of adaptable benchmark simulations to serve as a standard for future investigative
  efforts into supervised HDLSS. Finally, we produce a comprehensive comparison of
  the included algorithms across a range of benchmark simulations and real data applications.
authors:
- family-names: Bridgeford
  given-names: Eric
  email: ericwb95@gmail.com
repository: https://cranhaven.r-universe.dev
repository-code: https://github.com/neurodata/lol
commit: fe8be1364a5d7bc282deabe64befd3b5dcb76ed0
url: https://github.com/neurodata/lol
date-released: '2020-06-20'
contact:
- family-names: Bridgeford
  given-names: Eric
  email: ericwb95@gmail.com