Robust Online Tensor Completion for IoT Streaming Data Recovery

• Published in: IEEE transaction on neural networks and learning systems 2023-12, Vol.34 (12), p.10178-10192
• Main Authors: Liu, Chunsheng, Wu, Tao, Li, Zhifei, Ma, Tao, Huang, Jun
• Format: Article
• Language: English
• Subjects: Algorithms; Corruption; Data models; Data recovery; Internet of Things; Internet of Things (IoT); Mathematical analysis; Noise measurement; Optimization; Outliers (statistics); Real-time systems; Robustness (mathematics); Sparse matrices; streaming data recovery; tensor completion (TC); Tensors; Theoretical analysis; Time measurement; time-varying rank; User experience
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2022.3165076

Reliable data measurement is considered to be one of the critical ingredients for variant Internet of Things (IoT) applications. Gaining full knowledge of measurement data is becoming increasingly crucial to ensure a satisfactory user experience. However, data missing and corruption are inevitable in practical applications, which motivates us to study how to accurately recover the missing IoT measurement data in the presence of outliers. The data recovery problem can be formulated as a tensor completion (TC) problem. Existing TC methods are built on the assumption that the rank of the underlying tensor is fixed, which is not suitable for long data sequences in practice. Consequently, based on the characteristics of IoT streaming data, we assume that the data tensor lies in time-varying subspace, and an accurate estimate of the rank is a prerequisite for filling the missing entries and achieving robustness of the variations in both rank and noise. We built up an updatable framework based on dynamic CANDECOMP/PARAFAC (CP) decomposition. In addition, an efficient algorithm, called temporal multi-aspect streaming (T-MUST), is introduced to solve the optimization problem that originates in our developed model. It is worth noting that the proposed algorithm allows time-varying tensor rank and enables the rank changes could be detected and tracked automatically. Theoretical analysis indicates that T-MUST enjoys a geometric convergence rate. Numerical experiments conducted on various synthetic and real-world datasets empirically validate the superiority of the proposed T-MUST in both efficiency and effectiveness.