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sparseEigen  

Computation of Sparse Eigenvectors of a Matrix
View on CRAN: Click here


Download and install sparseEigen package within the R console
Install from CRAN:
install.packages("sparseEigen")

Install from Github:
library("remotes")
install_github("cran/sparseEigen")

Install by package version:
library("remotes")
install_version("sparseEigen", "0.1.0")



Attach the package and use:
library("sparseEigen")
Maintained by
Daniel P. Palomar
[Scholar Profile | Author Map]
First Published: 2017-12-21
Latest Update: 2017-12-21
Description:
Computation of sparse eigenvectors of a matrix (aka sparse PCA) with running time 2-3 orders of magnitude lower than existing methods and better final performance in terms of recovery of sparsity pattern and estimation of numerical values. Can handle covariance matrices as well as data matrices with real or complex-valued entries. Different levels of sparsity can be specified for each individual ordered eigenvector and the method is robust in parameter selection. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Sun, P. Babu, and D. P. Palomar (2016). "Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on Signal Processing <doi:10.1109/TSP.2016.2605073>.
How to cite:
Daniel P. Palomar (2017). sparseEigen: Computation of Sparse Eigenvectors of a Matrix. R package version 0.1.0, https://cran.r-project.org/web/packages/sparseEigen. Accessed 07 Apr. 2025.
Previous versions and publish date:
No previous versions
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Complete documentation for sparseEigen
Functions, R codes and Examples using the sparseEigen R package
Some associated functions: spEigen . spEigenCov . 
Some associated R codes: spEigen.R . spEigenCov.R .  Full sparseEigen package functions and examples
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