Low-Rank and Structured Sparse Tensor Decomposition for Anomaly Detection in Multivariate Functional Data
Multivariate functional data arise in many modern manufacturing systems, where multiple sensors record densely sampled process trajectories. Monitoring such data is challenging because nominal variation is strongly correlated across samples, sensors, and time, while faults may appear either as isolated deviations or as structured departures concentrated within a limited number of sensor-specific temporal trajectories. We propose two unsupervised sparse tensor decomposition methods that preserve this multimode structure. Entrywise Sparse CP Decomposition (ES-CP) uses an entrywise \(\ell_1\) penalty to identify localized anomalies, whereas Fiberwise Sparse-Group Lasso CP Decomposition (FG-L...
| Authors | Mohammad N. Bisheh, Che-Yi Liao, Kamran Paynabar |
|---|---|
| Primary category | stat.ML |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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