【摘 要】
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Nonnegative tensor ring (NTR) decomposition is a powerful tool for capturing the significant features of tensor objects while preserving the multi-linear structure of tensor data.The existing algorithms rely on frequent reshaping and permutation opera-tio
【机 构】
：
School of Automation,Guangdong University of Technology,Guangzhou 510006,China;School of Automation,
论文部分内容阅读
Nonnegative tensor ring (NTR) decomposition is a powerful tool for capturing the significant features of tensor objects while preserving the multi-linear structure of tensor data.The existing algorithms rely on frequent reshaping and permutation opera-tions in the optimization process and use a sbrinking step size or projection techniques to ensure core tensor nonnegativity,which leads to a slow convergence rate,especially for large-scale problems.In this paper,we first propose an NTR algorithm based on the modulus method (NTR-MM),which constrains core tensor nonnegativity by modulus transformation.Second,a low-rank approximation (LRA) is introduced to NTR-MM (named LRA-NTR-MM),which not only reduces the computational complexity of NTR-MM significantly but alsosuppresses the noise.The simulation results demonstrate that the proposed LRA-NTR-MM al-gorithm achieves higher computational efficiency than the state-of-the-art algorithms while preserving the effectiveness of feature extraction.
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