@InProceedings{8902816,
  author = {M. Boussé and N. Sidiropoulos and L. D. Lathauwer},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {NLS Algorithm for Kronecker-Structured Linear Systems with a CPD Constrained Solution},
  year = {2019},
  pages = {1-5},
  abstract = {In various applications within signal processing, system identification, pattern recognition, and scientific computing, the canonical polyadic decomposition (CPD) of a higher-order tensor is only known via general linear measurements. In this paper, we show that the computation of such a CPD can be reformulated as a sum of CPDs with linearly constrained factor matrices by assuming that the measurement matrix can be approximated by a sum of a (small) number of Kronecker products. By properly exploiting the hypothesized structure, we can derive an efficient non-linear least squares algorithm, allowing us to tackle large-scale problems.},
  keywords = {least squares approximations;linear systems;matrix decomposition;pattern recognition;signal processing;tensors;NLS algorithm;Kronecker-structured linear systems;CPD constrained solution;signal processing;system identification;pattern recognition;scientific computing;canonical polyadic decomposition;higher-order tensor;linearly constrained factor matrices;measurement matrix;nonlinear least squares algorithm;Tensors;Signal processing algorithms;Jacobian matrices;Signal processing;Linear systems;Computational complexity;Matrix decomposition},
  doi = {10.23919/EUSIPCO.2019.8902816},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570528074.pdf},
}

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