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.