Tensor Completion via Leverage Sampling and Tensor QR Decomposition for Network Latency Estimation

by   Jun Lei, et al.
Wenzhou University

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.


Tensor Completion via Tensor QR Decomposition and L_2,1-Norm Minimization

In this paper, we consider the tensor completion problem, which has many...

CTD: Fast, Accurate, and Interpretable Method for Static and Dynamic Tensor Decompositions

How can we find patterns and anomalies in a tensor, or multi-dimensional...

Grassmannian Optimization for Online Tensor Completion and Tracking in the t-SVD Algebra

We propose a new streaming algorithm, called TOUCAN, for the tensor comp...

Generative Modeling via Hierarchical Tensor Sketching

We propose a hierarchical tensor-network approach for approximating high...

Sampling-Based Decomposition Algorithms for Arbitrary Tensor Networks

We show how to develop sampling-based alternating least squares (ALS) al...

Rapid Detection of Hot-spots via Tensor Decomposition with applications to Crime Rate Data

We propose an efficient statistical method (denoted as SSR-Tensor) to ro...

Please sign up or login with your details

Forgot password? Click here to reset