UFS: Unsupervised Feature Selection

In hetcorFS: Unsupervised Feature Selection using the Heterogeneous Correlation Matrix

Unsupervised Feature Selection

Description

Performs unsupervised feature selection for mixed type data. Both algorithms are based on the heterogeneous correlation matrix.

Usage

UFS(
  data = NULL,
  alpha = 0.05,
  missing = FALSE,
  pv_adj = "none",
  smooth.tol = 10^-12,
  method = "c"
)

Arguments

data	A data frame. Values of type 'numeric' or 'integer' are treated as numerical, factors as ordinal categorical.
alpha	Significance level to be used for testing, default = 0.05.
missing	Pairwise complete by default, set to TRUE for complete deletion.
pv_adj	Correction method for p-value, "none" by default. For options see p.adjust.
smooth.tol	Minimum acceptable eigenvalue for the smoothing, default = 10^-12.
method	Algorithm used. c (cell-wise) by default, r (row-wise) as the alternative.

Value

An list of elements:

rearranged.data.set	Original data frame with with numerical features first
selected.features	A data frame of the selected features
feature.indices	The indices of the selected features from the original data frame
original.corr.matrix	The p by p extended correlation matrix of all the inputted features
corr.matrix	The d by d extended correlation matrix of the selected features
original.p.value.matrix	The p by p p-values matrix of all the inputted features
p.value.matrix	The d by d p-values matrix of the selected features

References

Tortora C., Madhvani S., Punzo A. (2025). Designing unsupervised mixed-type feature selection techniques using the heterogeneous correlation matrix. International Statistical Review. https://doi.org/10.1111/insr.70016

Examples

data(ESI)#Loading the data
data = ESI[,-c(1,3,4,6,9)]##removing categorical features
res = UFS(data)

### visualize selected features
colnames(res$selected.features)

hetcorFS documentation built on Nov. 24, 2025, 5:07 p.m.