Oct 9, 2019 · In this work, we propose a novel localized tensor completion model (LTC) to increase the data recovery accuracy by taking advantage of the ...
Our results demonstrate that LTC is very effective in increasing the tensor recovery accuracy without depending on specific tensor completion algorithms.
To well utilize the local data feature for more accurate data recovery, we propose a novel localized tensor completion model where we propose several novel ...
In this work, we propose a novel localized tensor completion model (LTC) to increase the data recovery accuracy by taking advantage of the stronger local ...
The simulation results demonstrate that our algorithm can achieve significantly better performance compared with the literature tensor and matrix completion ...
Missing: Localized | Show results with:Localized
对于各种网络工程任务，从部分测量数据推断网络流量数据变得越来越重要。通过利用多维数据结构，张量补全是一种很有前途的技术，可以更准确地推断缺失数据。
This paper is the first to apply the tensor to model Internet traffic data to well exploit their hidden structures and propose a sequential tensor ...
Another important application of SpTD is for the completion of missing data: we can regard the majority of zero entries in a tensor as missing data, and then ...
In this paper, we propose a network traffic recovery method which only requires the conveniently measurable link-load traffics.
This paper proposes a data recovery algorithm for large-scale network measurements—association learning based tensor completion(ALTC). To capture the complex ...