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Unsupervised Anomaly Detection in Multi-Aspect Data via Tensor Decomposition and Hidden Markov Models

  • 문혜원 (Moon Hyewon, 아주대학교 일반대학원)

초록/요약

Anomaly detection in unlabeled multi-aspect bio-signals with semi-periodic patterns is a challenging task. We propose a novel unsupervised anomaly scoring method called importance to effectively address this problem. Our approach combines Tucker decomposition and Gaussian Mixture Hidden Markov Models (GM-HMM) to simultaneously capture the latent patterns in the multi-aspect structure and the inherent temporal patterns of the data. The importance score’s novelty stems from 1) a new definition of error contribution from the input values, and 2) a weight definition for the temporal factor based on GM-HMM. This weighted error contribution enables more accurate anomaly detection compared to existing methods. Extensive experiments were conducted on synthetic multi-aspect time-series data to demonstrate the effectiveness of our importance score for anomaly detection compared to other approaches. Further evaluations on three real-world bio-signal datasets provide empirical evidence of the effectiveness in detecting unusual signals.

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목차

1 Introduction 1
1.1 Contributions 3
2 Fundamental 4
2.1 Tensor Operation 5
2.2 Tucker Decomposition 7
2.3 Hidden Markov Model 8
2.3.1 Gaussian Mixture - Hidden Markov Model (GM-HMM) 11
3 Method 14
3.1 Tensorizing and Decomposition Signal 15
3.2 Temporal Weight Calculation with GM-HMM 16
3.3 Importance 19
4 Experiment 21
4.1 Model Selection 23
4.2 Validation of Weight 24
4.3 Detecting of Anomalities in Bio-Signals via Importance Score 25
4.3.1 Anomaly Detection on Fetal ECG Dataset 25
4.3.2 Anomaly Detection on Sleep Stage Dataset 26
4.3.3 Anomaly Detection on VitalDB Dataset 27
5 Conclusion 29

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