Online joint classification and anomaly detection via sparse coding

We present a novel convex scheme for simultaneous online fault classification and anomaly detection in a multivariate time-series setting. Our approach extends recent work on sparse coding and anomaly detection using an over-complete dictionary to problems where some taxonomy of anomalies already ex...

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Bibliographic Details
Main Authors: Kalaitzis, Alfredo, Nelson, James D. B.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
anomaly detection
basis pursuit
classification
Dictionaries
Encoding
group LASSO
Matrix converters
Monitoring
multivariate time series
powerline monitoring
sparse coding
Training
Vectors
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Summary:We present a novel convex scheme for simultaneous online fault classification and anomaly detection in a multivariate time-series setting. Our approach extends recent work on sparse coding and anomaly detection using an over-complete dictionary to problems where some taxonomy of anomalies already exists. The temporal aspect of the data is addressed by a simple sliding window approach; inspired by a group-LASSO penalisation approach, classification is treated by jointly sparsifying groups of the coefficients (the sparse coding) of dictionary atoms via ℓ 2;1 regularisation. The dictionary which drives the prediction and coding is assumed given and is learnable by a range of available prior algorithms. We demonstrate our framework on a classification and anomaly detection task on three-phase low-voltage time-series. In this case, we manually design our dictionary based on basic knowledge of common faults that affect low-voltage powerlines. For this reason our approach does not necessarily require a training stage.
ISSN:1551-2541
2378-928X
DOI:10.1109/MLSP.2014.6958880