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:
sparseEigen_0.1.0.9000.tar.gz
sparseEigen_0.1.0.9000.zip(r-4.5)sparseEigen_0.1.0.9000.zip(r-4.4)sparseEigen_0.1.0.9000.zip(r-4.3)
sparseEigen_0.1.0.9000.tgz(r-4.4-any)sparseEigen_0.1.0.9000.tgz(r-4.3-any)
sparseEigen_0.1.0.9000.tar.gz(r-4.5-noble)sparseEigen_0.1.0.9000.tar.gz(r-4.4-noble)
sparseEigen_0.1.0.9000.tgz(r-4.4-emscripten)sparseEigen_0.1.0.9000.tgz(r-4.3-emscripten)
sparseEigen.pdf |sparseEigen.html✨
sparseEigen/json (API)
# Install 'sparseEigen' in R: |
install.packages('sparseEigen', repos = c('https://dppalomar.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/dppalomar/sparseeigen/issues
covariance-matrixeigenvectorspcasparse
Last updated 6 years agofrom:2120b0c0f9. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 14 2024 |
R-4.5-win | OK | Nov 14 2024 |
R-4.5-linux | OK | Nov 14 2024 |
R-4.4-win | OK | Nov 14 2024 |
R-4.4-mac | OK | Nov 14 2024 |
R-4.3-win | OK | Nov 14 2024 |
R-4.3-mac | OK | Nov 14 2024 |
Exports:spEigenspEigenCov
Dependencies:
Readme and manuals
Help Manual
Help page | Topics |
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sparseEigen: Computation of Sparse Eigenvectors of a Matrix | sparseEigen-package |
Sparse Spectral Decomposition of a Matrix | spEigen |
Covariance Matrix Estimation with Sparse Eigenvectors | spEigenCov |