Robust Anomaly Detection via Tensor Pseudoskeleton Decomposition

TL;DR

This paper introduces a robust anomaly detection method for high-dimensional tensor data using tensor pseudoskeleton decomposition within a tensor-robust PCA framework, demonstrating improved accuracy and stability over existing methods.

Contribution

It proposes a novel tensor pseudoskeleton decomposition approach integrated with tensor-robust PCA for effective anomaly detection in high-dimensional data, with theoretical guarantees and real-world validation.

Findings

The method accurately detects anomalies in NYC taxi data.

The approach outperforms benchmark methods in stability and accuracy.

Theoretical analysis confirms convergence and estimation error bounds.

Abstract

Anomaly detection plays a critical role in modern data-driven applications, from identifying fraudulent transactions and safeguarding network infrastructure to monitoring sensor systems for irregular patterns. Traditional approaches, such as distance, density, or cluster-based methods, face significant challenges when applied to high dimensional tensor data, where complex interdependencies across dimensions amplify noise and computational complexity. To address these limitations, this paper leverages Tensor Chidori pseudoskeleton decomposition within a tensor-robust principal component analysis framework to extract low Tucker rank structure while isolating sparse anomalies, ensuring robustness to anomaly detection. We establish theoretical results regarding convergence, and estimation error, demonstrating the stability and accuracy of the proposed approach. Numerical experiments on real-world spatiotemporal data from New York City taxi trip records validate the effectiveness of the proposed method in detecting anomalous urban events compared to existing benchmark methods. Our results suggest that tensor pseudoskeleton decomposition may offer potential for enhancing anomaly detection in large-scale, high-dimensional data.