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Department of Food Science - Chemometrics (University of Copenhagen)

Lovelace's Square

Lovelace's Square - SPARAFAC

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MIT | 1.0 | 2026-02-19

SPARAFAC

Feature SelectionUnmixingMATLAB

Morten Arendt Rasmussen [1]Rasmus Bro [1]

[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

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