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ICASSP
2011
IEEE

Detection of anomalous events from unlabeled sensor data in smart building environments

8 years 11 months ago
Detection of anomalous events from unlabeled sensor data in smart building environments
This paper presents a robust unsupervised learning approach for detection of anomalies in patterns of human behavior using multi-modal smart environment sensor data. We model the data using a Gaussian Mixture Model, where the features are weighted based on their discriminative ability and are simultaneously clustered. The number of clusters in this approach is automatically chosen using the Minimum Message Length (MML) criterion. The weight of non-discriminative features is driven towards zero which results in a form of dimensionality reduction. Our results indicate that, in practical applications involving unlabeled, high-dimensional multi-modal sensor data from smart building environments, feature weighting achieves higher accuracy in detecting anomalous events with lower false alarm rates compared to using traditional Gaussian Mixtures.
Padmini Jaikumar, Aca Gacic, Burton Andrews, Micha
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Padmini Jaikumar, Aca Gacic, Burton Andrews, Michael Dambier
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