Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis

@article{Cichocki2014TensorDF,
  title={Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis},
  author={Andrzej Cichocki and Danilo P. Mandic and Lieven De Lathauwer and Guoxu Zhou and Qibin Zhao and Cesar F. Caiafa and A. Phan},
  journal={IEEE Signal Processing Magazine},
  year={2014},
  volume={32},
  pages={145-163}
}
The widespread use of multisensor technology and the emergence of big data sets have highlighted the limitations of standard flat-view matrix models and the necessity to move toward more versatile data analysis tools. We show that higher-order tensors (i.e., multiway arrays) enable such a fundamental paradigm shift toward models that are essentially polynomial, the uniqueness of which, unlike the matrix methods, is guaranteed under very mild and natural conditions. Benefiting from the power of… 
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