Low-rank Approximation Based non-Negative Multi-Way Array Decomposition on Event-Related potentials

@article{Cong2014LowrankAB,
  title={Low-rank Approximation Based non-Negative Multi-Way Array Decomposition on Event-Related potentials},
  author={Fengyu Cong and Guoxu Zhou and Piia Astikainen and Qibin Zhao and Qiang Wu and Asoke K. Nandi and Jari K. Hietanen and Tapani Ristaniemi and Andrzej Cichocki},
  journal={International journal of neural systems},
  year={2014},
  volume={24 8},
  pages={
          1440005
        }
}
Non-negative tensor factorization (NTF) has been successfully applied to analyze event-related potentials (ERPs), and shown superiority in terms of capturing multi-domain features. However, the time-frequency representation of ERPs by higher-order tensors are usually large-scale, which prevents the popularity of most tensor factorization algorithms. To overcome this issue, we introduce a non-negative canonical polyadic decomposition (NCPD) based on low-rank approximation (LRA) and hierarchical… 

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