| l1spectral-package {l1spectral} | R Documentation |
Description of the package
Description
Provides an l1-version of the spectral clustering algorithm devoted to robustly clustering highly perturbed graphs using l1-penalty. This algorithm is described with more details in the preprint C. Champion, M. Champion, M. Blazère, R. Burcelin and J.M. Loubes, "l1-spectral clustering algorithm: a spectral clustering method using l1-regularization" (2022).
Details
l1-spectral clustering is an l1-penalized version of the spectral clustering algorithm, which aims at robustly detecting cluster structure of perturbed graphs by promoting sparse eigenbases solutions of specific l1-minimization problems.
The DESCRIPTION file:
| Package: | l1spectral |
| Title: | An L1-Version of the Spectral Clustering |
| Version: | 0.99.6 |
| Authors@R: | c(person("Camille", "Champion", role = "aut"), person("Magali", "Champion", role = c("aut","cre"),email="magali.champion@u-paris.fr" )) |
| Description: | Provides an l1-version of the spectral clustering algorithm devoted to robustly clustering highly perturbed graphs using l1-penalty. This algorithm is described with more details in the preprint C. Champion, M. Champion, M. Blazère, R. Burcelin and J.M. Loubes, "l1-spectral clustering algorithm: a spectral clustering method using l1-regularization" (2022). |
| License: | GPL-2 |
| Imports: | Rcpp (>= 0.12.5), stats, dplyr, graphics, igraph, Matrix, aricode, grDevices, caret, glmnet, ggplot2, cvTools |
| LinkingTo: | Rcpp, RcppArmadillo |
| Encoding: | UTF-8 |
| LazyData: | true |
| Roxygen: | list(markdown = TRUE) |
| RoxygenNote: | 7.1.2 |
| Author: | Camille Champion [aut], Magali Champion [aut, cre] |
| Maintainer: | Magali Champion <magali.champion@u-paris.fr> |
Author(s)
NA
References
C. Champion, M. Champion, M. Blazère, R. Burcelin, J.M. Loubes, l1-spectral clustering algorithm: a robust spectral clustering using Lasso regularization, Preprint (2021).
See Also
Examples
#####################################################
# Performing the l1-spectral clustering on the graph
#####################################################
data(ToyData)
# if desired, the number of clusters and representative elements can be provided,
# otherwise remove
results2 <- l1_spectralclustering(A = ToyData$A_hat, pen = "lasso")
results2$comm
# when desired, the number of clusters and representative elements can also be provided
results2 <- l1_spectralclustering(A = ToyData$A_hat, pen = "lasso",
k=2, elements = c(1,4))