Description Usage Arguments Details Value References Examples
Perform sparse matrix decomposition under group constraints.
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X |
n x p data matrix to preprocess. |
z |
n x 1 vector with group information. |
t_val |
penalty parameter. Must be a positive number. Control the strength of the l-1 penalty. |
K |
approximation rank. The defaul is |
control |
Control parameters for the fitting process. A list as returned by |
rescaled |
Should the matrix |
The function performs a sparse matrix decomposition of the input matrix X
with constraints on the left singular vectors and the group variable z
.
The output is a matrix U
of basis and a matrix S
of scores such that \tilde X = S * U^T, where \tilde X is the rank-K approximation of X
U
n x K matrix of scores.
S
K x p matrix of sparse loadings
Aliverti, Lum, Johndrow and Dunson (2018). Removing the influence of a group variable in high-dimensional predictive modelling (https://arxiv.org/abs/1810.08255).
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