Supervised Data Transformation by Means of Neural Network Hidden Layers

A supervised transformation of datasets is performed. The aim is similar to that of Principal Component Analysis (PCA), that is, to carry out data transformation and dimensionality reduction, but in a non-linear supervised way. This is achieved by first training a 3-layer Multi-Layer Perceptron and then using the activations of the hidden layer as a transformation of the input features. In fact, it takes advantage of the change of representation provided by the hidden layer of a neural network. This can be useful as data pre-processing for Machine Learning methods in general, specially for those that do not work well with many irrelevant or redundant features. Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986) "Learning representations by back-propagating errors" .


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("nntrf")

0.1.0 by Ricardo Aler, a month ago


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


Authors: Ricardo Aler [aut, cre] , Jose Valls [aut]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports nnet, NeuralNetTools, FNN, pracma

Suggests dplyr, knitr, rmarkdown, ggplot2, ggridges, tidyr, forcats


See at CRAN