Kronecker product approximation with multiple factor matrices via the tensor product algorithm

@article{Wu2016KroneckerPA,
  title={Kronecker product approximation with multiple factor matrices via the tensor product algorithm},
  author={King Keung Wu and Yeung Yam and Helen M. Meng and Mehran Mesbahi},
  journal={2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
  year={2016},
  pages={004277-004282}
}
  • King Keung Wu, Y. Yam, M. Mesbahi
  • Published 1 October 2016
  • Computer Science, Mathematics
  • 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Kronecker product (KP) approximation has recently been applied as a modeling and analysis tool on systems with hierarchical networked structure. In this paper, we propose a tensor product-based approach to the KP approximation problem with arbitrary number of factor matrices. The formulation involves a novel matrix-to-tensor transformation to convert the KP approximation problem to a best rank-(R1, …, RN) tensor product approximation problem. In addition, we develop an algorithm based on higher… 

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