Nonnegative low-rank tensor completion method for spatiotemporal traffic data
Nonnegative low-rank tensor completion method for spatiotemporal traffic data
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DOI：
10.1007/s11042-023-15511-w
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发表时间：
2023-05
影响因子：
3.6
通讯作者：
Yongmei Zhao;M. Tuo;Hongmei Zhang;Han Zhang;Jiangnan Wu;Fengyin Gao
中科院分区：
文献类型：
--
作者：
Yongmei Zhao;M. Tuo;Hongmei Zhang;Han Zhang;Jiangnan Wu;Fengyin Gao
针对张量补全理论在数据缺失率较高的情况下表现良好，但在数据补全的非负约束层面缺乏足够的关注，缺乏有效的非负张量补全方法的问题，提出了一种基于低秩张量补全理论的非负张量补全模型--非负加权低秩张量补全（Nonnegative Weighted Low-Rank Tensor Completion，NWLRTC）方法，本文提出由于截断核范数（TNN）在低秩近似方面的优势，NWLRTC将TNN作为目标优化函数，并在模型中添加方向权重因子，以避免其对数据输入方向的依赖。NWLRTC除了考虑完井精度外，还引入了非负约束，以满足实际工程应用的要求。最后，用交替方向乘法器法实现了NWLRTC。至于实验，他们进行了使用不同的方法来产生缺失数据和不同的迭代时间。实验结果表明，NWLRTC算法在低数据丢失率下具有较高的完成精度，即使在数据丢失率达到80%时也能保持稳定的完成精度。
Although tensor completion theory performs well with high data missing rates, a lack of attention is encountered at the level of data completion non-negative constraints, and a remaining lack of effective non-negative tensor completion methods is still found. In this article, a new non-negative tensor completion model, based on the low-rank tensor completion theory, called the Nonnegative Weighted Low-Rank Tensor Completion (NWLRTC) method, is proposed. Due to the advantages of Truncated Nuclear Norm (TNN) in low-rank approximation, NWLRTC considers the TNN as the objective optimization function and adds a directional weight factor to the model to avoid its dependency on the data input direction. In addition to considering the completion accuracy, NWLRTC also imposes non-negativity constraints to meet the requirements of practical engineering applications. Finally, NWLRTC is realized by the alternating direction multiplier method. As for the experiments, they are carried out using different methods for generating missing data and for different iteration times. The experimental results show that the NWLRTC algorithm has high completion accuracy at low missing data rates, and it maintains a stable completion accuracy even when the missing rate hits 80%.