spcaWrapper {scPCA} | R Documentation |
This wrapper function specifies which implementation of sparse
pricincipal component analysis (SPCA) is used to sparsify the loadings of
the contrastive covariance matrix. Currently, the scPCA
package
supports the iterative algorithm detailed by
Zou et al. (2006), and
Erichson et al. (2018)'s randomized and non-randomized
versions of SPCA solved via variable projection. These methods are
implemented in the elasticnet and sparsepca packages.
spcaWrapper( alg, contrast_cov, contrast, k, penalty, eigdecomp_tol, eigdecomp_iter )
alg |
A |
contrast_cov |
A contrastive covariance |
contrast |
A |
k |
A |
penalty |
A |
eigdecomp_tol |
A |
eigdecomp_iter |
A |
A p x k
sparse loadings matrix, where p
is the
number of features, and k
is the number of sparse contrastive
components.
Erichson NB, Zeng P, Manohar K, Brunton SL, Kutz JN, Aravkin AY (2018).
“Sparse Principal Component Analysis via Variable Projection.”
ArXiv, abs/1804.00341.
Zou H, Hastie T, Tibshirani R (2006).
“Sparse principal component analysis.”
Journal of computational and graphical statistics, 15(2), 265–286.