= HOSVD-based canonical form of TP functions and qLPV models
{wiki=HOSVD-based_canonical_form_of_TP_functions_and_qLPV_models}
The HOSVD (Higher-Order Singular Value Decomposition) is a mathematical tool used in tensor decomposition, which is particularly useful in the fields of control theory, signal processing, and machine learning for tasks involving multi-way data or tensor representations. In the context of Tensor Product (TP) functions and quasi-linear parameter-varying (qLPV) models, the HOSVD can be applied to represent these complex systems in a more compact and interpretable form.
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