gsvd: Generalised Singular Value Decomposition - GSVD

In multiblock: Multiblock Data Fusion in Statistics and Machine Learning

Generalised Singular Value Decomposition - GSVD

Description

This is a wrapper for the geigen::gsvd function for computing GSVD.

Usage

gsvd(X)

Arguments

X	list of input data blocks.

Details

GSVD is a generalisation of SVD to two variable-linked matrices where common loadings and block-wise scores are estimated.

Value

multiblock object with associated with printing, scores, loadings. Relevant plotting functions: multiblock_plots and result functions: multiblock_results.

References

Van Loan, C. (1976) Generalizing the singular value decomposition. SIAM Journal on Numerical Analysis, 13, 76–83.

See Also

Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex. Common functions for computation and extraction of results and plotting are found in multiblock_results and multiblock_plots, respectively.

Examples

data(potato)
X <- potato$Chemical

gsvd.pot <- gsvd(lapply(potato[3:4], t))

multiblock documentation built on Nov. 18, 2023, 5:06 p.m.