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Arxiv
1d
372
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A Double-Norm Aggregated Tensor Latent Factorization Model for Temporal-Aware Traffic Speed Imputation
- Researchers propose a Temporal-Aware Traffic Speed Imputation model for intelligent transportation systems.
- The model uses a combination of L2-norm and smooth L1-norm in its loss function.
- It adopts a single latent factor-dependent, nonnegative, and multiplicative update approach.
- Empirical studies demonstrate that the model achieves accurate imputations for missing traffic speed data.
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