Linear Optimal Low-Rank Projection

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) , 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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("lolR")

2.0 by Eric Bridgeford, a year ago


https://github.com/neurodata/lol


Browse source code at https://github.com/cran/lolR


Authors: Eric Bridgeford [aut, cre] , Minh Tang [ctb] , Jason Yim [ctb] , Joshua Vogelstein [ths]


Documentation:   PDF Manual  


GPL-2 license


Imports ggplot2, abind, MASS, irlba, pls

Suggests knitr, rmarkdown, parallel, randomForest, latex2exp, testthat, covr


See at CRAN