[1]Department of Food Science, University of Copenhagen, Denmark
SPARAFAC (Sparse PARAFAC) performs variable selection in high-dimensional multi-way models by inducing sparsity through L1 norm penalized minimization (Lasso). Uses Alternating Shrunken Least Squares (ASLS) with soft thresholding, enabling multidimensional co-clustering. Supports non-negativity and orthogonality constraints. Multiple random starts are recommended as the non-convex optimization may converge to local minima.
Reference:Rasmussen, M. A.; Bro, R. (2012). A tutorial on the Lasso approach to sparse modeling. Chemometrics and Intelligent Laboratory Systems, 119, 21-31. https://doi.org/10.1016/j.chemolab.2012.10.003
