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

@article{Cichocki2015TensorDF,
  title={Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis},
  author={Andrzej Cichocki and Danilo P. Mandic and Anh Huy Phan and Cesar F. Caiafa and Guoxu Zhou and Qibin Zhao and Lieven De Lathauwer},
  journal={IEEE Signal Processing Magazine},
  year={2015},
  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… CONTINUE READING

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