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|___location=Copenhagen, Denmark
|year=2002
|url= https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=67eb22ae0baf86af77188fc0ab27edacf07a9140}}</ref><ref name=Vasilescu2003/><ref name=":Vasilescu2005">{{cite conference
|author=M. A. O. Vasilescu, D. Terzopoulos
|title=Multilinear Independent Component Analysis
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=== Interlacing computation ===
A strategy that is significantly faster when some or all <math>R_m \ll I_m </math> consists of interlacing the computation of the core tensor and the factor matrices, as follows:<ref name=
|title=Multilinear Subspace Analysis for Image Ensembles
|last1=Vasilescu
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* Compute a rank-<math>\bar R_m </math> truncated SVD <math>\mathcal{A}_{[m]} \approx U_m \Sigma_m V^T_m </math>, and store the top <math>\bar R_m </math> left singular vectors <math>U_m \in F^{I_m \times \bar R_m}</math>;
while a '''sequentially truncated M-mode SVD (HOSVD)''' (or '''successively truncated M-mode SVD(HOSVD)''') is obtained by replacing step 2 in the interlaced computation by
* Compute a rank-<math>\bar R_m </math> truncated SVD <math>\mathcal{A}_{[m]}^{m-1} \approx U_m \Sigma_m V^T_m </math>, and store the top <math>\bar R_m </math> left singular vectors <math>U_m \in F^{I_m \times \bar R_m}</math>. Unfortunately, truncation does not result in an optimal solution for the best low multilinear rank optimization problem,.<ref name=":2" /><ref name=
== Applications ==
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