SGD_Tucker: A Novel Stochastic Optimization Strategy for Parallel Sparse Tucker Decomposition

@article{Li2021SGD\_TuckerAN,
  title={SGD\_Tucker: A Novel Stochastic Optimization Strategy for Parallel Sparse Tucker Decomposition},
  author={Hao Li and Zixuan Li and KenLi Li and Jan S. Rellermeyer and Lydia Yiyu Chen and Keqin Li},
  journal={IEEE Trans. Parallel Distributed Syst.},
  year={2021},
  volume={32},
  pages={1828-1841}
}
  • Hao Li, Zixuan Li, Keqin Li
  • Published 7 December 2020
  • Computer Science
  • IEEE Trans. Parallel Distributed Syst.
Sparse Tucker Decomposition (STD) algorithms learn a core tensor and a group of factor matrices to obtain an optimal low-rank representation feature for the \underline{H}igh-\underline{O}rder, \underline{H}igh-\underline{D}imension, and \underline{S}parse \underline{T}ensor (HOHDST). However, existing STD algorithms face the problem of intermediate variables explosion which results from the fact that the formation of those variables, i.e., matrices Khatri-Rao product, Kronecker product, and… 
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