A Parameter-free Nonconvex Low-rank Tensor Completion Model for Spatiotemporal Traffic Data Recovery. (arXiv:2209.13786v1 [cs.LG])

Traffic data chronically suffer from missing and corruption, leading to
accuracy and utility reduction in subsequent Intelligent Transportation System
(ITS) applications. Noticing the inherent low-rank property of traffic data,
numerous studies formulated missing traffic data recovery as a low-rank tensor
completion (LRTC) problem. Due to the non-convexity and discreteness of the
rank minimization in LRTC, existing methods either replaced rank with convex
surrogates that are quite far away from the rank function or approximated rank
with nonconvex surrogates involving many …

arxiv data data recovery free low recovery tensor traffic