Tensor Completion via Leverage Sampling and Tensor QR Decomposition for
  Network Latency Estimation
Jun Lei Ji-Qian Zhao Jing-Qi Wang An-Bao Xu
In this paper, we consider the network latency estimation, which has been an
important metric for network performance. However, a large scale of network
latency estimation requires a lot of computing time. Therefore, we propose a
new method that is much faster and maintains high accuracy. The data structure
of network nodes can form a matrix, and the tensor model can be formed by
introducing the time dimension. Thus, the entire problem can be be summarized
as a tensor completion problem. The main idea of our method is improving the
tensor leverage sampling strategy and introduce tensor QR decomposition into
tensor completion. To achieve faster tensor leverage sampling, we replace
tensor singular decomposition (t-SVD) with tensor CSVD-QR to appoximate t-SVD.
To achieve faster completion for incomplete tensor, we use the tensor
$L_{2,1}$-norm rather than traditional tensor nuclear norm. Furthermore, we
introduce tensor QR decomposition into alternating direction method of
multipliers (ADMM) framework. Numerical experiments witness that our method is
faster than state-of-art algorithms with satisfactory accuracy.