Automated Hyperparameter Optimization for Tensor Factorization in Dynamic Network Analysis
Researchers propose DE-LFT, an automated framework using Differential Evolution to optimize hyperparameters for latent factorization of tensors applied to large-scale dynamic weighted directed networks. Current methods rely on manual tuning or computationally expensive grid search, which limits practical applicability. The approach demonstrates improved prediction accuracy with reduced computational overhead on real-world datasets.
This arXiv paper addresses a practical challenge in machine learning: the computational burden of hyperparameter tuning for tensor factorization models used in network analysis. The authors propose DE-LFT, which integrates Differential Evolution into the training process to automatically learn optimal regularization parameters (λ₁, λ₂, λ₃) for latent factorization of tensors. Rather than relying on manual parameter selection or exhaustive grid search, the method adaptively searches the hyperparameter space during model training. Experimental validation on four real-world datasets shows the approach achieves lower mean absolute error (MAE) and root mean squared error (RMSE) compared to manually tuned baselines. This work contributes to making tensor-based representation learning more practical for large-scale dynamic network applications by reducing both computational cost and human effort.
What's missing
The paper does not discuss computational complexity or wall-clock time comparisons between DE-LFT and grid search methods. Additionally, the specific characteristics of the four real-world datasets used for evaluation are not detailed in the abstract, limiting assessment of generalizability. The paper also does not address potential limitations of Differential Evolution as an optimization method or discuss convergence guarantees.
What different sources said
- arXiv cs.LGCenter
Hyperparameter Learning for Latent Factorization of Tensors for Representation Learning to Large-scale Dynamic Weighted Directed Network
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