An Incomplete Tensor Tucker decomposition based Traffic Speed Prediction Method. (arXiv:2304.10961v1 [cs.LG])

In intelligent transport systems, it is common and inevitable with missing
data. While complete and valid traffic speed data is of great importance to
intelligent transportation systems. A latent factorization-of-tensors (LFT)
model is one of the most attractive approaches to solve missing traffic data
recovery due to its well-scalability. A LFT model achieves optimization usually
via a stochastic gradient descent (SGD) solver, however, the SGD-based LFT
suffers from slow convergence. To deal with this issue, this work integrates
the unique …

advantages arxiv convergence data data recovery deal factorization gradient importance intelligent optimization prediction recovery scalability solver speed stochastic systems tensor traffic transport transportation tucker work