A Nonlocal Similarity Learning-Based Tensor Completion Model With Its Application in Intelligent Transportation System
Cheng Dai, Ying Zhang, Zhigao Zheng
Abstract
Predicting the traffic flow has been one of the most important applications in intelligent transportation system. However, the missing information in the traffic data will directly affect the final performance, and it has evolved into a challenge in data analysis based intelligent transportation system applications. Recently, Nuclear Norm-based Tensor Completion algorithm can recover the missing multidimensional information in traffic data recovery by truncated nuclear norm minimization. However, the existing Truncated Nuclear Norm threshold may result in excessive punishment of large singular values, and therefore leading the accurate data missing. To overcome this problem, we present a new Nuclear Norm-based Tensor Completion method, which considers the prior rank information and retains large singular values to approximate the rank of the matrix better. Firstly, to achieve optimized rank parameter, a similar block matrix method is proposed, which takes advantage of the nonlocal similarity to the separate of data and noise. Moreover, an optimal rank estimation algorithm is proposed, which can automatically achieve the truncated threshold parameter through the iterative optimization algorithm. Finally, extensive experiments have been conducted to verify our proposal, and the results show that our method can get better performance in terms of recovery accuracy.