# 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} }

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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