Higher-order tensors have become popular in many areas of applied mathematics such as statistics, scientific computing, signal processing or machine learning, notably thanks to the many possible ways of decomposing a tensor. In this paper, we focus on the best approximation in the least-squares sense of a higher-order tensor by a block term decomposition. Using variable projection, we express the tensor approximation problem as a minimization of a cost function on a Cartesian product of Stiefel manifolds. The effect of variable projection on the Riemannian gradient algorithm is studied through numerical experiments.
CITATION STYLE
Olikier, G., Absil, P. A., & De Lathauwer, L. (2018). Variable projection applied to block term decomposition of higher-order tensors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10891 LNCS, pp. 139–148). Springer Verlag. https://doi.org/10.1007/978-3-319-93764-9_14
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