Description Usage Arguments Value Author(s) Examples
Groups sparse Generalized SVD of a matrix
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X |
a (data) matrix; |
R |
the desired rank of the singular decomposition; |
au |
The radiuses (>0) of the $L_1$ ball for each left vector. Default to the maximum possible radius, such that the result is the same as the result of a regular SVD. |
av |
The radiuses (>0) of the $L_1$ balls for each right vector. Default to the maximum possible radius, such that the result is the same as the result of a regular SVD. |
Gu |
a vector describing the groups for the lines. |
Gv |
a vector describing the groups for the columns. |
itermax.pi |
The maximum number of iterations for the power iteration. |
itermax.pocs |
The maximum number of iterations for POCS. |
eps.pi |
Precision for the power iteration. |
eps.pocs |
Precision for POCS. |
init |
How to initialize the algorithm. Either "svd" (default) or "rand" to intialize with, respectively, the results of a regular SVD or random vectors. |
order_sv |
Boolean. Should the singular values be artificially ordered. Default to TRUE. |
Pseudo-singular vectors (U and V) and values (D), and the number of iterations.
Vincent Guillemot
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