Alternating least squares is often used to resolve components contributing to data with a bilinear structure; the basic technique may be extended to alternating constrained least squares. Commonly applied constraints include unimodality, non-negativity, and normalization of components. Several data matrices may be decomposed simultaneously by assuming that one of the two matrices in the bilinear decomposition is shared between datasets.
|Author||Katharine M. Mullen|
|Date of publication||2015-08-03 11:44:35|
|Maintainer||Katharine Mullen <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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