Package: sparseEigen 0.1.0.9000

sparseEigen: Computation of Sparse Eigenvectors of a Matrix

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, "Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on Signal Processing, IEEE Trans. on Signal Processing, vol. 64, no. 23, pp. 6211-6226, Dec. 2016. <doi:10.1109/TSP.2016.2605073>.

Authors:Konstantinos Benidis [aut], Daniel P. Palomar [cre, aut]

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sparseEigen/json (API)

# Install 'sparseEigen' in R:
install.packages('sparseEigen', repos = c('https://dppalomar.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/dppalomar/sparseeigen/issues

On CRAN:

covariance-matrixeigenvectorspcasparse

5.42 score 12 stars 22 scripts 379 downloads 2 exports 0 dependencies

Last updated 6 years agofrom:2120b0c0f9. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winOKOct 31 2024
R-4.5-linuxOKOct 31 2024
R-4.4-winOKOct 31 2024
R-4.4-macOKOct 31 2024
R-4.3-winOKOct 31 2024
R-4.3-macOKOct 31 2024

Exports:spEigenspEigenCov

Dependencies:

Computing Sparse Eigenvectors of a Matrix

Rendered fromSparseEigenvectors-vignette.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2018-05-03
Started: 2018-05-03