TRPCA
An extension of Robust Principal Component Analysis to tensor data. It separates normal components from anomalous components through low-rank and sparse decomposition and is commonly used as a baseline method for anomaly detection in multi-dimensional data.
Timeline (last 90 days)
In the same case study, TRPCA and PCA-based anomaly detectors achieved only 0.69 or lower macro F1 scores
Traditional methods such as PCA and TRPCA tend to flatten the data or ignore its inherent multimodal tensor structure, thereby failing when confronted with complex faults involving sensor-time correlations
All Facts (2)
In the same case study, TRPCA and PCA-based anomaly detectors achieved only 0.69 or lower macro F1 scores
50%UnverifiedTraditional methods such as PCA and TRPCA tend to flatten the data or ignore its inherent multimodal tensor structure, thereby failing when confronted with complex faults involving sensor-time correlations
50%