Baranyi and Yam employed the ideas described by De Lathauwer etal[6] and the algorithm developed by Vasilescu and Terzopoulos under the name M-mode SVD.[7][8] The M-mode SVD is referred in the literature as either the Tucker or the HOSVD. The Tucker algorithm and the DeLathauwer etal. companion algorithm[9] are sequential algorithm that employ gradient descent or the power method, respectively.
Related definitions (on TP functions, finite element TP functions, and TP models) can be found here. Details on the control theoretical background (i.e., the TP type polytopic Linear Parameter-Varying state-space model) can be found here.
A free MATLAB implementation of the TP model transformation can be downloaded at [1] or at MATLAB Central [2].
Existence of the M-mode SVD/HOSVD canonical form
Assume a given finite element TP function:
where . Assume that, the weighting functions in are othonormal (or we transform to) for . Then, the execution of the HOSVD on the core tensor leads to:
Then,
that is:
where weighting functions of are orthonormed (as both the and where orthonormed) and core tensor contains the higher-order singular values.
Definition
HOSVD (M-moe SVD) canonical form of TP function
Singular functions of : The weighting functions , (termed as the -th singular function on the -th dimension, ) in vector form an orthonormal set:
all-orthogonality: two sub tensors and are orthogonal for all possible values of and when ,
&* ordering: for all possible values of .
-mode singular values of : The Frobenius-norm , symbolized by , are -mode singular values of and, hence, the given TP function.
is termed core tensor.
The -mode rank of : The rank in dimension denoted by equals the number of non-zero singular values in dimension .
References
^P. Baranyi and L. Szeidl and P. Várlaki and Y. Yam (July 3–5, 2006). "Definition of the HOSVD-based canonical form of polytopic dynamic models". 3rd International Conference on Mechatronics (ICM 2006). Budapest, Hungary. pp. 660–665.
^P. Baranyi, Y. Yam and P. Várlaki (2013). Tensor Product model transformation in polytopic model-based control. Boca Raton FL: Taylor & Francis. p. 240. ISBN978-1-43-981816-9.
^P. Baranyi (April 2004). "TP model transformation as a way to LMI based controller design". IEEE Transactions on Industrial Electronics. 51 (2): 387–400. doi:10.1109/tie.2003.822037. S2CID7957799.
^P. Baranyi and D. Tikk and Y. Yam and R. J. Patton (2003). "From Differential Equations to PDC Controller Design via Numerical Transformation". Computers in Industry. 51 (3): 281–297. doi:10.1016/s0166-3615(03)00058-7.
^M. A. O. Vasilescu, D. Terzopoulos (2005). "Multilinear Independent Component Analysis". Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR’05). San Diego, CA.
^De Lathauwer, L.; De Moor, B.; Vandewalle, J. (2000-01-01). "On the Best Rank-1 and Rank-(R1 ,R2 ,. . .,RN) Approximation of Higher-Order Tensors". SIAM Journal on Matrix Analysis and Applications. 21 (4): 1324–1342. CiteSeerX10.1.1.102.9135. doi:10.1137/S0895479898346995. ISSN0895-4798.