sparseEigen-package: sparseEigen: Computation of Sparse Eigenvectors of a Matrix
In dppalomar/sparseEigen: Computation of Sparse Eigenvectors of a Matrix

Description Functions Help Author(s) References

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.

spEigen, spEigenCov

For a quick help see the README: GitHub-README and CRAN-README.

For more details see the vignette: GitHub-html-vignette, GitHub-pdf-vignette, and CRAN-pdf-vignette.

Konstantinos Benidis and Daniel P. Palomar

K. Benidis, Y. Sun, P. Babu, and D. P. Palomar, "Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on Signal Processing, vol. 64, no. 23, pp. 6211-6226, Dec. 2016.

dppalomar/sparseEigen documentation built on May 5, 2019, 12:31 p.m.