2018 • Conference Paper
A variable projection method for block term decomposition of higher-order tensors
Authors:
Olikier, Guillaume,
Absil, Pierre-Antoine ,
De Lathauwer, Lieven
Published in:
26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
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. We present numerical experiments where variable projection makes a steepest-descent method approximately twice faster