A Robust and Non-Iterative Tensor Decomposition Method with Automatic Thresholding
This provides a fully automatic framework for tensor decomposition, reducing computational costs and dependence on expertise, which is incremental but useful for applications like IoT and biometric sensing.
The paper tackles the challenge of accurate and efficient low-rank approximation for high-dimensional tensor data by proposing a non-iterative method that automatically determines ranks using statistical thresholding, outperforming conventional approaches in estimation accuracy and computational efficiency.
Recent advances in IoT and biometric sensing technologies have led to the generation of massive and high-dimensional tensor data, yet achieving accurate and efficient low-rank approximation remains a major challenge. Most existing tensor decomposition methods require predefined ranks and iterative optimization, resulting in high computational costs and dependence on analyst expertise. This study proposes a novel tensor low-rank approximation method that eliminates both prior rank specification and iterative optimization. The method applies statistical singular value hard thresholding to each mode-wise unfolded matrix to automatically extract statistically significant components, effectively reducing noise while preserving the intrinsic structure. Theoretically, the optimal thresholds for each mode are derived from the asymptotic properties of the Marcenko-Pastur distribution. Simulation experiments demonstrate that the proposed method outperforms conventional approaches (HOSVD, HOOI, and Tucker-L2E) in both estimation accuracy and computational efficiency. These results indicate that the proposed approach provides a theoretically grounded, fully automatic, and non-iterative framework for tensor decomposition.